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.
267 lines
8.2 KiB
Python
267 lines
8.2 KiB
Python
"""
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Web Scraping Example
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This example demonstrates how to scrape web content and build knowledge bases using Semantica.
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Key Features Demonstrated:
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- Web content scraping and crawling
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- HTML content parsing and cleaning
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- Web content normalization
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- Entity extraction from web content
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- Relationship detection in web content
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- Knowledge base construction from web data
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Use Cases:
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- News article processing
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- Blog post analysis
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- Website content extraction
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- Social media content processing
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- E-commerce product information extraction
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"""
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class WebScrapingExample:
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"""
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Web scraping example implementation.
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This example shows how to:
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• Scrape web content from various sources
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• Parse HTML content and extract text
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• Normalize and clean web content
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• Extract entities and relationships
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• Build knowledge bases from web data
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• Handle different web content types
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"""
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def __init__(self):
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"""
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Initialize web scraping example.
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• Setup Semantica framework
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• Configure web scraping tools
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• Initialize content parsing
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• Setup knowledge extraction
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• Configure export handlers
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"""
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# TODO: Initialize Semantica framework
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# TODO: Setup WebIngestor for web scraping
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# TODO: Configure HTML parsing
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# TODO: Setup content normalization
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# TODO: Initialize entity extraction
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# TODO: Setup relationship detection
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pass
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def scrape_website(self, website_url, **options):
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"""
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Scrape content from a website.
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• Scrape web content from specified URL
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• Parse HTML content and extract text
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• Normalize and clean content
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• Extract entities and relationships
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• Build knowledge base
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• Export results
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Args:
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website_url: URL of website to scrape
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**options: Scraping options
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Returns:
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dict: Scraping results including knowledge base
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"""
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# TODO: Use WebIngestor to scrape website
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# TODO: Parse HTML content using WebParser
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# TODO: Normalize content 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 base
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# TODO: Return scraping results
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pass
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def scrape_multiple_pages(self, page_urls, **options):
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"""
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Scrape content from multiple web pages.
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• Scrape multiple pages concurrently
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• Process each page individually
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• Aggregate results from all pages
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• Build comprehensive knowledge base
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Args:
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page_urls: List of page URLs to scrape
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**options: Scraping options
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Returns:
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dict: Aggregated scraping results
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"""
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# TODO: Use WebIngestor to scrape multiple pages
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# TODO: Process each page concurrently
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# TODO: Aggregate results from all pages
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# TODO: Build comprehensive knowledge base
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# TODO: Return aggregated results
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pass
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def scrape_with_sitemap(self, sitemap_url, **options):
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"""
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Scrape website using sitemap.
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• Parse sitemap to get page URLs
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• Scrape all pages in sitemap
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• Process sitemap structure
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• Build comprehensive knowledge base
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Args:
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sitemap_url: URL of website sitemap
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**options: Scraping options
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Returns:
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dict: Sitemap-based scraping results
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"""
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# TODO: Use SitemapCrawler to parse sitemap
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# TODO: Extract page URLs from sitemap
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# TODO: Scrape all pages in sitemap
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# TODO: Process sitemap structure
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# TODO: Build comprehensive knowledge base
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# TODO: Return sitemap-based results
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pass
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def process_news_articles(self, news_urls, **options):
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"""
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Process news articles from web sources.
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• Scrape news article content
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• Extract article metadata
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• Process article text content
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• Extract entities and relationships
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• Build news knowledge base
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Args:
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news_urls: List of news article URLs
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**options: News processing options
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Returns:
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dict: News processing results
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"""
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# TODO: Scrape news article content
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# TODO: Extract article metadata (title, date, author)
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# TODO: Process article text content
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# TODO: Extract entities and relationships
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# TODO: Build news knowledge base
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# TODO: Return news processing results
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pass
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def process_blog_posts(self, blog_urls, **options):
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"""
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Process blog posts from web sources.
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• Scrape blog post content
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• Extract post metadata
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• Process post text content
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• Extract entities and relationships
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• Build blog knowledge base
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Args:
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blog_urls: List of blog post URLs
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**options: Blog processing options
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Returns:
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dict: Blog processing results
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"""
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# TODO: Scrape blog post content
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# TODO: Extract post metadata (title, date, author, tags)
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# TODO: Process post text content
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# TODO: Extract entities and relationships
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# TODO: Build blog knowledge base
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# TODO: Return blog processing results
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pass
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def extract_web_entities(self, web_content, **options):
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"""
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Extract entities from web content.
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• Process web content for entity extraction
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• Extract named entities
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• Classify entity types
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• Handle entity disambiguation
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Args:
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web_content: Web content to process
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**options: Entity extraction options
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Returns:
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dict: Entity extraction results
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"""
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# TODO: Use NamedEntityRecognizer to extract entities
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# TODO: Classify entity types
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# TODO: Handle entity disambiguation
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# TODO: Return entity extraction results
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pass
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def extract_web_relationships(self, web_content, entities, **options):
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"""
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Extract relationships from web content.
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• Process web content for relationship extraction
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• Extract relationships between entities
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• Classify relationship types
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• Handle relationship disambiguation
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Args:
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web_content: Web content to process
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entities: Extracted entities
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**options: Relationship extraction options
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Returns:
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dict: Relationship extraction results
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"""
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# TODO: Use RelationExtractor to extract relationships
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# TODO: Classify relationship types
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# TODO: Handle relationship disambiguation
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# TODO: Return relationship extraction results
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pass
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def run_web_scraping_example():
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"""
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Run the web scraping example.
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This function demonstrates the complete web scraping workflow.
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"""
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# TODO: Create WebScrapingExample instance
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# TODO: Define website URLs to scrape
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# TODO: Scrape website content
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# TODO: Extract knowledge from web content
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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_news_scraping_example():
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"""
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Run news article scraping example.
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This function demonstrates news article processing.
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"""
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# TODO: Create WebScrapingExample instance
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# TODO: Define news article URLs
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# TODO: Scrape news articles
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# TODO: Extract knowledge from news
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# TODO: Export news processing results
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# TODO: Display results
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pass
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def run_blog_scraping_example():
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"""
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Run blog post scraping example.
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This function demonstrates blog post processing.
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"""
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# TODO: Create WebScrapingExample instance
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# TODO: Define blog post URLs
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# TODO: Scrape blog posts
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# TODO: Extract knowledge from blog posts
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# TODO: Export blog processing results
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# TODO: Display results
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pass
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