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feat: Implement comprehensive Semantica framework structure

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- 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.
This commit is contained in:
KaifAhmad1
2025-10-22 22:47:05 +05:30
parent 149d558279
commit aa10b9434c
66 changed files with 11726 additions and 1606 deletions
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cookbook/basic_examples/document_processing_example.py
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"""
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