mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
KaifAhmad1
aa10b9434c
- Add complete libs/semantica framework with 20+ production-ready modules - Implement core orchestration, configuration, and plugin management - Add comprehensive data ingestion (files, web, streams, databases, emails) - Implement parsing for documents, web content, structured data, code, media - Add data normalization (text, entities, dates, numbers, quality) - Implement semantic extraction (NER, relations, events, coreference, triples) - Add ontology management and knowledge graph construction - Implement graph analytics (centrality, community detection, connectivity) - Add embeddings generation and vector store management - Implement pipeline orchestration and streaming processing - Add security (access control, data masking, PII redaction) - Implement quality assurance and validation systems - Add export capabilities (RDF, JSON, CSV, graph formats) - Create comprehensive cookbook with examples and use cases - Add basic examples (document processing, web scraping, knowledge graphs) - Add advanced examples (multi-modal processing, real-time analytics) - Implement detailed bullet-point comments throughout - Follow SDK best practices and Python-only implementation - Add comprehensive pyproject.toml with dependencies and configuration - Include detailed README with usage examples and documentation This commit establishes the complete foundation for the Semantica semantic layer and knowledge engineering framework.
217 lines
6.8 KiB
Python
217 lines
6.8 KiB
Python
"""
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Document Processing Example
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This example demonstrates how to process documents and extract knowledge using Semantica.
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Key Features Demonstrated:
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- Document ingestion from various sources
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- Document parsing and content extraction
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- Text normalization and cleaning
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- Entity extraction and relationship detection
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- Knowledge graph construction
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- Export to various formats
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Use Cases:
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- Academic paper processing
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- Legal document analysis
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- Technical documentation processing
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- Research paper knowledge extraction
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"""
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class DocumentProcessingExample:
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"""
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Document processing example implementation.
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This example shows how to:
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• Ingest documents from various sources
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• Parse and extract content from different formats
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• Normalize and clean text content
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• Extract entities and relationships
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• Build knowledge graphs
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• Export results to various formats
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"""
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def __init__(self):
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"""
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Initialize document processing example.
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• Setup Semantica framework
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• Configure document processing pipeline
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• Initialize knowledge extraction tools
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• Setup export handlers
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"""
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# TODO: Initialize Semantica framework
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# TODO: Setup document processing pipeline
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# TODO: Configure entity extraction
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# TODO: Setup relationship detection
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# TODO: Initialize knowledge graph builder
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pass
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def process_documents(self, document_paths, **options):
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"""
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Process documents and extract knowledge.
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• Ingest documents from specified paths
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• Parse documents and extract content
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• Normalize and clean text content
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• Extract entities and relationships
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• Build knowledge graph
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• Export results to various formats
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Args:
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document_paths: List of document file paths
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**options: Additional processing options
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Returns:
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dict: Processing results including knowledge graph and metrics
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"""
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# TODO: Ingest documents using FileIngestor
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# TODO: Parse documents using DocumentParser
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# TODO: Normalize text using TextNormalizer
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# TODO: Extract entities using NamedEntityRecognizer
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# TODO: Extract relationships using RelationExtractor
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# TODO: Build knowledge graph using KnowledgeGraphBuilder
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# TODO: Export results using various exporters
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# TODO: Return processing results
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pass
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def process_pdf_documents(self, pdf_paths, **options):
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"""
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Process PDF documents specifically.
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• Handle PDF-specific parsing
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• Extract text and metadata
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• Process embedded content
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• Handle multi-page documents
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Args:
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pdf_paths: List of PDF file paths
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**options: PDF processing options
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Returns:
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dict: PDF processing results
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"""
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# TODO: Use PDFParser for PDF-specific processing
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# TODO: Extract text content and metadata
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# TODO: Handle embedded images and tables
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# TODO: Process multi-page documents
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# TODO: Return PDF processing results
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pass
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def process_word_documents(self, docx_paths, **options):
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"""
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Process Word documents specifically.
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• Handle DOCX-specific parsing
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• Extract text and formatting
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• Process embedded objects
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• Handle document structure
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Args:
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docx_paths: List of DOCX file paths
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**options: DOCX processing options
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Returns:
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dict: DOCX processing results
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"""
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# TODO: Use DOCXParser for DOCX-specific processing
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# TODO: Extract text content and formatting
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# TODO: Handle embedded objects and tables
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# TODO: Process document structure
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# TODO: Return DOCX processing results
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pass
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def extract_knowledge(self, processed_documents, **options):
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"""
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Extract knowledge from processed documents.
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• Extract entities and relationships
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• Build knowledge graph
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• Identify knowledge patterns
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• Generate knowledge insights
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Args:
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processed_documents: Processed document data
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**options: Knowledge extraction options
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Returns:
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dict: Knowledge extraction results
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"""
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# TODO: Extract entities using NamedEntityRecognizer
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# TODO: Extract relationships using RelationExtractor
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# TODO: Build knowledge graph using KnowledgeGraphBuilder
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# TODO: Identify knowledge patterns
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# TODO: Generate knowledge insights
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# TODO: Return knowledge extraction results
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pass
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def export_results(self, knowledge_graph, export_formats, **options):
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"""
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Export knowledge graph to various formats.
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• Export to RDF formats
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• Export to JSON formats
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• Export to CSV formats
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• Generate reports
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Args:
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knowledge_graph: Knowledge graph to export
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export_formats: List of export formats
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**options: Export options
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Returns:
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dict: Export results
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"""
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# TODO: Export to RDF using RDFExporter
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# TODO: Export to JSON using JSONExporter
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# TODO: Export to CSV using CSVExporter
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# TODO: Generate reports using ReportGenerator
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# TODO: Return export results
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pass
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def run_document_processing_example():
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"""
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Run the document processing example.
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This function demonstrates the complete document processing workflow.
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"""
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# TODO: Create DocumentProcessingExample instance
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# TODO: Define document paths
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# TODO: Process documents
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# TODO: Extract knowledge
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# TODO: Export results
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# TODO: Display results
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pass
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def run_pdf_processing_example():
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"""
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Run PDF document processing example.
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This function demonstrates PDF-specific processing.
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"""
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# TODO: Create DocumentProcessingExample instance
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# TODO: Define PDF document paths
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# TODO: Process PDF documents
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# TODO: Extract knowledge from PDFs
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# TODO: Export PDF processing results
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# TODO: Display results
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pass
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def run_word_processing_example():
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"""
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Run Word document processing example.
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This function demonstrates DOCX-specific processing.
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"""
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# TODO: Create DocumentProcessingExample instance
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# TODO: Define DOCX document paths
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# TODO: Process DOCX documents
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# TODO: Extract knowledge from DOCX files
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# TODO: Export DOCX processing results
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# TODO: Display results
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pass
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