diff --git a/README.md b/README.md index 3008069f..04223c4b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# 🧠 SemantiCore +# 🧠 SemantiCore
@@ -9,7 +9,9 @@ [![Docker](https://img.shields.io/badge/docker-ready-blue?style=for-the-badge&logo=docker&logoColor=white)](https://hub.docker.com/r/semanticore/semanticore) [![Kubernetes](https://img.shields.io/badge/kubernetes-ready-326CE5?style=for-the-badge&logo=kubernetes&logoColor=white)](https://kubernetes.io/) -**πŸš€ Transform unstructured data into structured semantic layers for LLMs, Agents, RAG systems, and Knowledge Graphs.** +**πŸš€ The Ultimate Semantic Data Transformation Platform** + +*Transform any unstructured data format into intelligent, structured semantic knowledge graphs, embeddings, and ontologies for LLMs, Agents, RAG systems, and Knowledge Graphs.* [πŸ“– Documentation](https://semanticore.readthedocs.io/) β€’ [πŸš€ Quick Start](#-quick-start) β€’ [πŸ’‘ Examples](#-use-cases--examples) β€’ [🀝 Community](#-community--support) β€’ [πŸ”§ API Reference](https://semanticore.readthedocs.io/api/) @@ -19,48 +21,50 @@ ## 🌟 What is SemantiCore? -SemantiCore bridges the gap between raw unstructured data and intelligent AI systems by providing a comprehensive toolkit for semantic extraction, schema generation, and knowledge representation. Built for developers creating AI agents, RAG systems, and intelligent applications that need to understand **meaning**, not just text. +SemantiCore is the most comprehensive semantic data transformation platform that bridges the gap between raw unstructured data in **any format** and intelligent AI systems. From complex documents to live data feeds, SemantiCore extracts meaning, builds knowledge, and creates intelligent semantic layers that power next-generation AI applications. -> **"The missing piece between your data and AI"** - Transform messy, unstructured information into clean, schema-compliant semantic layers that power next-generation AI applications. +> **"The missing link between your data and AI β€” turning unstructured chaos into structured, intelligent semantic knowledge."** ### 🎯 Why Choose SemantiCore? -Modern AI systems require structured, semantically rich data to perform effectively. SemantiCore solves the fundamental challenge of converting unstructured information into intelligent, actionable knowledge: - @@ -70,93 +74,59 @@ Modern AI systems require structured, semantically rich data to perform effectiv ## ✨ Core Capabilities +### πŸ“Š **Universal Data Ingestion & Processing** +- **Document Formats**: PDF, DOCX, XLSX, PPTX, TXT, RTF, ODT, EPUB, LaTeX +- **Web Formats**: HTML, XML, XHTML, RSS, Atom, JSON-LD, Sitemap XML +- **Structured Data**: JSON, YAML, CSV, TSV, Parquet, Avro, ORC +- **Markup Languages**: Markdown, ReStructuredText, AsciiDoc, Confluence +- **Feed Processing**: RSS/Atom feeds, news feeds, social media streams +- **Email Formats**: EML, MSG, MBOX, PST archives +- **Archive Formats**: ZIP, TAR, RAR, 7Z with recursive processing +- **Database Exports**: SQL dumps, MongoDB exports, CouchDB +- **Scientific Formats**: BibTeX, EndNote, RIS, JATS XML +- **Code Repositories**: Git repositories, documentation, README files + ### 🧠 **Advanced Semantic Processing** - **Multi-layer Understanding**: Lexical, syntactic, semantic, and pragmatic analysis - **Entity & Relationship Extraction**: Named entities, relationships, and complex event detection +- **Automatic Triple Generation**: Subject-Predicate-Object triples from any content - **Context Preservation**: Maintain semantic context across document boundaries -- **Domain Adaptation**: Specialized processing for cybersecurity, finance, healthcare, research - **Temporal Analysis**: Time-aware semantic understanding and event sequencing - -### 🎯 **LLM Optimization & Integration** -- **Context Engineering**: Intelligent context compression and enhancement for LLMs -- **Prompt Optimization**: Semantic-aware prompt engineering and optimization -- **Memory Management**: Episodic, semantic, and procedural memory systems -- **Multi-Model Support**: OpenAI, Anthropic, Google Gemini, Hugging Face, local models -- **Token Efficiency**: Smart token usage optimization for cost reduction +- **Cross-Document Linking**: Entity resolution and relationship mapping across sources +- **Ontology Alignment**: Automatic mapping to existing ontologies (Schema.org, FOAF, Dublin Core) ### πŸ•ΈοΈ **Knowledge Graph Construction** -- **Automated Construction**: Build knowledge graphs from unstructured data -- **Graph Databases**: Neo4j, KuzuDB, ArangoDB, Amazon Neptune integration +- **Automated Construction**: Build knowledge graphs from any data format +- **Triple Stores**: Blazegraph, Virtuoso, Apache Jena, GraphDB integration +- **Graph Databases**: Neo4j, KuzuDB, ArangoDB, Amazon Neptune, TigerGraph - **Semantic Reasoning**: Inductive, deductive, and abductive reasoning capabilities -- **Temporal Modeling**: Time-aware relationships and evolution tracking +- **Ontology Generation**: Automatic OWL/RDF ontology creation from data patterns - **Graph Analytics**: Centrality analysis, community detection, path finding +- **SPARQL Query Generation**: Automatic query generation for semantic search -### πŸ“Š **Vector & Embedding Excellence** -- **Contextual Embeddings**: Semantic embeddings with preserved context -- **Vector Stores**: Pinecone, Milvus, Weaviate, Chroma, FAISS integration -- **Hybrid Search**: Combine semantic and keyword search strategies -- **Embedding Models**: OpenAI, Cohere, Sentence Transformers, custom models -- **Semantic Similarity**: Advanced similarity metrics and clustering - -### πŸ”— **Ontology & Schema Generation** -- **Automated Ontology Creation**: Generate OWL/RDF ontologies from data +### πŸ“ˆ **Intelligent Content Transformation** +- **Semantic Chunking**: Context-aware document segmentation for RAG systems +- **Multi-Modal Embeddings**: Text, image, table, and chart embeddings - **Schema Evolution**: Dynamic schema adaptation and versioning -- **Standard Compliance**: Schema.org, FIBO, domain-specific ontologies -- **Multi-format Export**: OWL, RDF, JSON-LD, Turtle formats -- **Type Safety**: Strong typing with Pydantic models +- **Content Enrichment**: Automatic metadata extraction and enhancement +- **Cross-Reference Resolution**: Link resolution across documents and formats +- **Summarization**: Extractive and abstractive summarization with semantic preservation -### πŸ€– **Agent Integration & Orchestration** -- **Semantic Routing**: Intelligent request routing based on semantic understanding -- **Agent Orchestration**: Coordinate multiple AI agents with shared semantic context -- **Framework Integration**: LangChain, LlamaIndex, CrewAI, AutoGen compatibility -- **Real-time Processing**: Stream processing for live data semantic analysis -- **Multi-Agent Systems**: Collaborative AI agent ecosystems +### πŸ” **Advanced Text & Content Analysis** +- **Topic Modeling**: LDA, BERTopic, hierarchical topic discovery +- **Sentiment Analysis**: Document, sentence, and aspect-level sentiment +- **Language Detection**: 100+ languages with confidence scoring +- **Content Classification**: Automatic categorization and tagging +- **Duplicate Detection**: Semantic similarity and near-duplicate identification +- **Information Extraction**: Tables, figures, citations, references -### πŸ—οΈ **Enterprise-Ready Features** -- **Scalability**: Horizontal scaling with distributed processing -- **Security**: End-to-end encryption and privacy protection -- **Monitoring**: Comprehensive observability and metrics -- **Compliance**: SOC2, GDPR, HIPAA compliance support -- **High Availability**: 99.9% uptime SLA with redundancy - ---- - -## πŸ†• **Data Processing & Transformation Modules** - -### πŸ“ **Advanced Document Processing** -- **Multi-Format Support**: PDF, DOCX, PPTX, XLSX, ODT, RTF, TXT, Markdown -- **Smart Content Extraction**: Text, tables, images, metadata, annotations -- **Layout Analysis**: Document structure understanding and preservation -- **OCR Integration**: Tesseract, PaddleOCR, AWS Textract, Google Vision -- **Document Classification**: Automatic categorization and routing - -### 🌐 **Web & RSS Feed Processing** -- **Intelligent Web Scraping**: JavaScript rendering, dynamic content extraction -- **RSS/Atom Feed Monitoring**: Real-time feed processing and semantic analysis -- **News Aggregation**: Multi-source news semantic analysis and deduplication -- **Content Monitoring**: Change detection and semantic diff analysis -- **Web Archive Processing**: Wayback Machine and historical content analysis - -### πŸ“Š **Structured Data Transformation** -- **Database Integration**: SQL, NoSQL, GraphQL, REST API data extraction -- **Spreadsheet Processing**: Complex Excel formulas, pivot tables, charts -- **CSV/TSV Advanced Processing**: Schema inference, data cleaning, validation -- **JSON/XML Deep Processing**: Nested structure flattening and semantic mapping -- **API Response Transformation**: Dynamic schema generation from API responses - -### 🎨 **Rich Media Processing** -- **HTML/CSS Semantic Extraction**: Clean text extraction with structure preservation -- **Markdown Advanced Processing**: Complex syntax, extensions, table processing -- **Email Processing**: MIME, headers, attachments, thread reconstruction -- **Social Media Data**: Twitter, LinkedIn, Reddit post processing and analysis -- **Code Repository Analysis**: Git history, code comments, documentation extraction - -### πŸ”„ **Real-time Stream Processing** -- **Message Queue Integration**: Kafka, RabbitMQ, AWS SQS, Redis Streams -- **Webhook Processing**: Real-time API callbacks and event processing -- **Log File Analysis**: System logs, application logs, security logs -- **Sensor Data Processing**: IoT data streams and time-series analysis -- **Social Media Streams**: Real-time social media monitoring and analysis +### 🌐 **Live Data Processing** +- **RSS/Atom Feed Monitoring**: Real-time feed processing and semantic extraction +- **Web Scraping**: Intelligent web content extraction with semantic understanding +- **API Integration**: REST, GraphQL, WebSocket real-time data processing +- **Stream Processing**: Kafka, RabbitMQ, Pulsar integration +- **Social Media Feeds**: Twitter, LinkedIn, Reddit semantic monitoring +- **News Aggregation**: Multi-source news processing and semantic analysis --- @@ -164,785 +134,681 @@ Modern AI systems require structured, semantically rich data to perform effectiv ### πŸ“¦ Installation Options -
-🐍 Python Installation - ```bash -# Basic installation -pip install semanticore - -# Full installation with all dependencies +# Complete installation with all format support pip install "semanticore[all]" -# Extended processing modules -pip install "semanticore[extended,web,feeds,documents,media]" +# Lightweight installation +pip install semanticore -# Specific integrations -pip install "semanticore[openai,neo4j,pinecone,ocr,scraping]" +# Specific format support +pip install "semanticore[pdf,web,feeds,office]" # Development installation -git clone https://github.com/yourusername/semanticore.git +git clone https://github.com/semanticore/semanticore.git cd semanticore -pip install -e ".[dev,extended]" +pip install -e ".[dev]" ``` -
-
-🐳 Docker Installation - -```bash -# Pull and run SemantiCore Extended -docker run -p 8000:8000 semanticore/semanticore:extended - -# With all processing modules -docker run -v ./config:/app/config -v ./data:/app/data semanticore/semanticore:full - -# Docker Compose for full stack with processing modules -curl -O https://raw.githubusercontent.com/semanticore/semanticore/main/docker-compose-extended.yml -docker-compose -f docker-compose-extended.yml up -d -``` -
- -### ⚑ 30-Second Extended Demo +### ⚑ 30-Second Demo: From Any Format to Knowledge ```python from semanticore import SemantiCore -from semanticore.processors import DocumentProcessor, WebProcessor, FeedProcessor -# Initialize with extended capabilities +# Initialize with preferred providers core = SemantiCore( llm_provider="openai", embedding_model="text-embedding-3-large", vector_store="pinecone", - graph_db="neo4j", - extended_processing=True + graph_db="neo4j" ) -# Process various data formats -doc_processor = DocumentProcessor(core) -web_processor = WebProcessor(core) -feed_processor = FeedProcessor(core) - -# Process PDF documents with OCR -pdf_result = doc_processor.process_pdf( +# Process ANY data format +sources = [ "financial_report.pdf", - enable_ocr=True, - extract_tables=True, - preserve_layout=True -) + "https://example.com/news/rss", + "research_papers/", + "data.json", + "https://example.com/article" +] -# Process web pages with JavaScript rendering -web_result = web_processor.scrape_and_process( - "https://news.example.com/article", - render_js=True, - extract_metadata=True, - follow_links=True -) +# One-line semantic transformation +knowledge_base = core.build_knowledge_base(sources) -# Process RSS feeds with semantic analysis -feed_result = feed_processor.monitor_feeds([ - "https://feeds.example.com/tech-news", - "https://feeds.example.com/financial-news" -], semantic_deduplication=True) +print(f"Processed {len(knowledge_base.documents)} documents") +print(f"Extracted {len(knowledge_base.entities)} entities") +print(f"Generated {len(knowledge_base.triples)} semantic triples") +print(f"Created {len(knowledge_base.embeddings)} vector embeddings") -# Unified semantic extraction across all sources -combined_semantics = core.extract_unified_semantics([ - pdf_result, web_result, feed_result -]) - -print("Total entities:", len(combined_semantics.entities)) -print("Cross-source relationships:", len(combined_semantics.cross_relations)) -print("Unified knowledge graph nodes:", len(combined_semantics.graph.nodes)) +# Query the knowledge base +results = knowledge_base.query("What are the key financial trends?") ``` --- -## 🧩 Extended Processing Modules +## πŸ”§ Data Processing Modules -### πŸ“„ Advanced Document Processing +### πŸ“„ Document Processing Module + +Process complex document formats with semantic understanding: ```python -from semanticore.processors.documents import ( - PDFProcessor, DOCXProcessor, ExcelProcessor, - MarkdownProcessor, HTMLProcessor, EmailProcessor -) +from semanticore.processors import DocumentProcessor -# PDF Processing with Advanced Features -pdf_processor = PDFProcessor( - ocr_engine="tesseract", # or "paddleocr", "aws_textract" - extract_images=True, +# Initialize document processor +doc_processor = DocumentProcessor( extract_tables=True, - preserve_layout=True, - language_detection=True + extract_images=True, + extract_metadata=True, + preserve_structure=True ) -# Process complex PDF with semantic extraction -pdf_result = pdf_processor.process( - "complex_document.pdf", - semantic_extraction=True, - chunk_by_sections=True, - extract_metadata=True -) +# Process various document types +pdf_content = doc_processor.process_pdf("report.pdf") +docx_content = doc_processor.process_docx("document.docx") +pptx_content = doc_processor.process_pptx("presentation.pptx") +excel_content = doc_processor.process_excel("data.xlsx") -print("Extracted text sections:", len(pdf_result.sections)) -print("Tables found:", len(pdf_result.tables)) -print("Images extracted:", len(pdf_result.images)) -print("Semantic entities:", len(pdf_result.entities)) - -# DOCX Processing with Style Preservation -docx_processor = DOCXProcessor( - preserve_formatting=True, - extract_comments=True, - extract_tracked_changes=True -) - -docx_result = docx_processor.process("document.docx") - -# Excel Processing with Formula Analysis -excel_processor = ExcelProcessor( - evaluate_formulas=True, - extract_charts=True, - analyze_pivot_tables=True -) - -excel_result = excel_processor.process("data.xlsx") -print("Sheets processed:", len(excel_result.sheets)) -print("Formulas analyzed:", len(excel_result.formulas)) +# Extract semantic information +for content in [pdf_content, docx_content, pptx_content]: + semantics = core.extract_semantics(content) + triples = core.generate_triples(semantics) + embeddings = core.create_embeddings(content.chunks) ``` -### 🌐 Web Scraping & RSS Processing +### 🌐 Web & Feed Processing Module + +Real-time web content and feed processing: ```python -from semanticore.processors.web import ( - WebScraper, RSSFeedProcessor, NewsAggregator, - SocialMediaProcessor, WebArchiveProcessor +from semanticore.processors import WebProcessor, FeedProcessor + +# Web content processor +web_processor = WebProcessor( + respect_robots=True, + extract_metadata=True, + follow_redirects=True, + max_depth=3 ) -# Advanced Web Scraping -web_scraper = WebScraper( - render_javascript=True, - wait_for_elements=True, - handle_dynamic_content=True, - extract_structured_data=True, # JSON-LD, microdata - follow_pagination=True +# RSS/Atom feed processor +feed_processor = FeedProcessor( + update_interval="5m", + deduplicate=True, + extract_full_content=True ) -# Scrape with semantic understanding -scrape_result = web_scraper.scrape_urls([ - "https://example.com/news", - "https://example.com/research" -], semantic_extraction=True) +# Process web content +webpage = web_processor.process_url("https://example.com/article") +semantics = core.extract_semantics(webpage.content) -# RSS Feed Processing with Intelligence -rss_processor = RSSFeedProcessor( - deduplication_strategy="semantic", - content_enrichment=True, - sentiment_analysis=True, - trend_detection=True -) +# Monitor RSS feeds +feeds = [ + "https://feeds.feedburner.com/TechCrunch", + "https://rss.cnn.com/rss/edition.rss", + "https://feeds.reuters.com/reuters/topNews" +] -# Monitor multiple feeds -feed_results = rss_processor.monitor_feeds({ - "tech_news": "https://feeds.example.com/tech", - "finance_news": "https://feeds.example.com/finance", - "security_news": "https://feeds.example.com/security" -}, update_interval=300) # 5 minutes - -# News Aggregation with Semantic Clustering -news_aggregator = NewsAggregator( - sources=["rss", "web", "api"], - clustering_method="semantic", - bias_detection=True, - fact_checking=True -) - -aggregated_news = news_aggregator.aggregate_and_analyze( - topics=["artificial intelligence", "cybersecurity", "finance"] -) - -print("News clusters:", len(aggregated_news.clusters)) -print("Bias scores:", aggregated_news.bias_analysis) +for feed_url in feeds: + feed_processor.subscribe(feed_url) + +# Process new feed items +async for item in feed_processor.stream_items(): + semantics = core.extract_semantics(item.content) + knowledge_graph.add_triples(core.generate_triples(semantics)) ``` -### πŸ“Š Structured Data Processing +### πŸ“Š Structured Data Processing Module + +Handle structured and semi-structured data formats: ```python -from semanticore.processors.structured import ( - DatabaseProcessor, APIProcessor, SpreadsheetProcessor, - JSONProcessor, XMLProcessor, CSVProcessor -) +from semanticore.processors import StructuredDataProcessor -# Database Processing -db_processor = DatabaseProcessor( - connection_string="postgresql://user:pass@localhost/db", - semantic_mapping=True, - relationship_inference=True -) - -# Extract semantic knowledge from database -db_semantics = db_processor.extract_semantics( - tables=["customers", "orders", "products"], - include_relationships=True, +# Initialize structured data processor +structured_processor = StructuredDataProcessor( + infer_schema=True, + extract_relationships=True, generate_ontology=True ) -# API Processing with Dynamic Schema Generation -api_processor = APIProcessor( - base_url="https://api.example.com", - authentication="bearer_token", - schema_inference=True, - semantic_mapping=True -) +# Process various structured formats +json_data = structured_processor.process_json("data.json") +csv_data = structured_processor.process_csv("dataset.csv") +yaml_data = structured_processor.process_yaml("config.yaml") +xml_data = structured_processor.process_xml("data.xml") -# Process API responses semantically -api_results = api_processor.process_endpoints([ - "/users", "/orders", "/products" -], semantic_extraction=True) - -# Advanced CSV Processing -csv_processor = CSVProcessor( - auto_detect_schema=True, - data_quality_assessment=True, - semantic_type_inference=True, - outlier_detection=True -) - -csv_result = csv_processor.process( - "large_dataset.csv", - chunk_size=10000, - parallel_processing=True -) - -print("Schema inferred:", csv_result.schema) -print("Data quality score:", csv_result.quality_score) -print("Semantic types:", csv_result.semantic_types) +# Extract semantic relationships +for data in [json_data, csv_data, yaml_data, xml_data]: + schema = structured_processor.generate_schema(data) + triples = structured_processor.extract_triples(data, schema) + ontology = structured_processor.create_ontology(schema) ``` -### 🎨 Rich Media & Content Processing +### πŸ“§ Email & Archive Processing Module + +Process email archives and compressed files: ```python -from semanticore.processors.media import ( - HTMLProcessor, MarkdownProcessor, EmailProcessor, - SocialMediaProcessor, CodeRepositoryProcessor -) +from semanticore.processors import EmailProcessor, ArchiveProcessor -# HTML Processing with Semantic Structure -html_processor = HTMLProcessor( - preserve_structure=True, - extract_microdata=True, - semantic_tagging=True, - content_classification=True -) - -html_result = html_processor.process( - html_content, - extract_links=True, - analyze_seo=True -) - -# Advanced Markdown Processing -markdown_processor = MarkdownProcessor( - extensions=["tables", "footnotes", "toc", "math"], - semantic_header_analysis=True, - cross_reference_linking=True -) - -md_result = markdown_processor.process("README.md") - -# Email Processing with Thread Analysis +# Email processing email_processor = EmailProcessor( - thread_reconstruction=True, - attachment_processing=True, - sentiment_analysis=True, - entity_recognition=True + extract_attachments=True, + parse_headers=True, + thread_detection=True ) -# Process email data -email_result = email_processor.process_mailbox( - "path/to/mailbox", - semantic_threading=True +# Archive processing +archive_processor = ArchiveProcessor( + recursive=True, + supported_formats=['zip', 'tar', 'rar', '7z'], + max_depth=5 ) -# Social Media Processing -social_processor = SocialMediaProcessor( - platforms=["twitter", "linkedin", "reddit"], - sentiment_analysis=True, - trend_detection=True, - influence_analysis=True -) +# Process email archives +mbox_data = email_processor.process_mbox("emails.mbox") +pst_data = email_processor.process_pst("outlook.pst") -social_result = social_processor.process_posts( - posts_data, - extract_hashtags=True, - network_analysis=True -) +# Process compressed archives +archive_contents = archive_processor.process_archive("documents.zip") -# Code Repository Analysis -code_processor = CodeRepositoryProcessor( - languages=["python", "javascript", "java"], - analyze_comments=True, - extract_documentation=True, - dependency_analysis=True -) - -repo_result = code_processor.process_repository( - "path/to/repo", - include_git_history=True -) +# Extract semantic information from all contents +for content in archive_contents: + semantics = core.extract_semantics(content) + triples = core.generate_triples(semantics) ``` -### πŸ”„ Real-time Stream Processing +### πŸ”¬ Scientific & Academic Processing Module + +Specialized processing for academic and scientific content: ```python -from semanticore.processors.streaming import ( - KafkaProcessor, WebhookProcessor, LogProcessor, - SensorDataProcessor, SocialStreamProcessor +from semanticore.processors import AcademicProcessor + +# Academic content processor +academic_processor = AcademicProcessor( + extract_citations=True, + parse_references=True, + identify_sections=True, + extract_figures=True ) -# Kafka Stream Processing -kafka_processor = KafkaProcessor( - bootstrap_servers=["localhost:9092"], - topics=["events", "logs", "metrics"], - semantic_processing=True, - real_time_analysis=True -) +# Process academic formats +latex_content = academic_processor.process_latex("paper.tex") +bibtex_content = academic_processor.process_bibtex("references.bib") +jats_content = academic_processor.process_jats("article.xml") -# Process streaming data with semantic analysis -async def process_kafka_stream(): - async for message in kafka_processor.stream(): - semantic_result = core.extract_semantics(message.value) - - if semantic_result.importance == "critical": - await alert_system.send_alert(semantic_result) - - # Update knowledge graph in real-time - core.update_knowledge_graph(semantic_result) - -# Webhook Processing -webhook_processor = WebhookProcessor( - endpoint="/webhooks/semantic", - authentication="hmac_sha256", - semantic_routing=True -) - -# Log File Processing -log_processor = LogProcessor( - log_formats=["apache", "nginx", "syslog", "json"], - anomaly_detection=True, - pattern_recognition=True, - semantic_correlation=True -) - -log_analysis = log_processor.process_logs( - "path/to/logs", - real_time=True, - alert_on_anomalies=True -) - -# IoT Sensor Data Processing -sensor_processor = SensorDataProcessor( - data_types=["temperature", "humidity", "pressure"], - time_series_analysis=True, - anomaly_detection=True, - semantic_context=True -) - -sensor_result = sensor_processor.process_stream( - sensor_data_stream, - window_size="5m", - aggregation_functions=["mean", "max", "std"] -) +# Extract academic semantic triples +for content in [latex_content, bibtex_content, jats_content]: + academic_semantics = academic_processor.extract_academic_entities(content) + citation_graph = academic_processor.build_citation_network(content) + research_triples = academic_processor.generate_research_triples(content) ``` --- -## πŸ”§ Extended Integration Examples +## 🧩 Semantic Extraction & Transformation -### 🌐 Multi-Source Data Pipeline +### 🎯 Automatic Triple Generation + +Generate RDF triples from any content automatically: ```python -from semanticore import SemantiCore -from semanticore.pipelines import UnifiedDataPipeline +from semanticore.extraction import TripleExtractor -# Create unified processing pipeline -pipeline = UnifiedDataPipeline( - sources={ - "documents": ["./docs/*.pdf", "./docs/*.docx"], - "web_pages": ["https://news.example.com", "https://research.example.com"], - "rss_feeds": ["https://feeds.example.com/tech", "https://feeds.example.com/science"], - "databases": ["postgresql://localhost/db1", "mongodb://localhost/db2"], - "apis": ["https://api.example.com/v1", "https://api2.example.com/v2"], - "streams": ["kafka://localhost:9092/events", "webhook://localhost:8080/data"] - }, - processors={ - "semantic_extraction": True, - "entity_linking": True, - "relationship_inference": True, - "ontology_mapping": True, - "quality_assessment": True - }, - output_formats=["knowledge_graph", "vector_embeddings", "structured_json"] -) - -# Process all sources -unified_result = pipeline.process_all_sources( - parallel_processing=True, - batch_size=100, - semantic_deduplication=True -) - -print(f"Processed {unified_result.total_documents} documents") -print(f"Extracted {len(unified_result.entities)} unique entities") -print(f"Found {len(unified_result.relationships)} relationships") -print(f"Generated knowledge graph with {unified_result.graph.node_count} nodes") -``` - -### πŸ”„ Real-time Multi-Format Processing - -```python -from semanticore.processors.realtime import RealTimeProcessor - -# Real-time processing of multiple formats -real_time_processor = RealTimeProcessor( - input_formats=["pdf", "html", "json", "csv", "xml", "rss"], - processing_modes=["streaming", "batch", "hybrid"], - semantic_analysis=True, - quality_monitoring=True -) - -# Set up processing rules -real_time_processor.add_processing_rule( - format="pdf", - condition="size > 10MB", - action="queue_for_batch_processing" -) - -real_time_processor.add_processing_rule( - format="json", - condition="contains_sensitive_data == True", - action="encrypt_and_process" -) - -# Start real-time processing -async def start_processing(): - async for processed_item in real_time_processor.process(): - # Route based on content type and semantic analysis - if processed_item.content_type == "financial": - await financial_system.process(processed_item) - elif processed_item.content_type == "security": - await security_system.process(processed_item) - else: - await general_system.process(processed_item) -``` - -### πŸ“Š Advanced Analytics Pipeline - -```python -from semanticore.analytics import SemanticAnalyticsPipeline - -# Create analytics pipeline for processed data -analytics_pipeline = SemanticAnalyticsPipeline( - data_sources=unified_result, - analytics_modules=[ - "trend_analysis", - "sentiment_analysis", - "topic_modeling", - "entity_clustering", - "relationship_analysis", - "temporal_analysis", - "cross_source_correlation" - ] -) - -# Run comprehensive analytics -analytics_result = analytics_pipeline.analyze( - time_window="30d", +# Initialize triple extractor +triple_extractor = TripleExtractor( confidence_threshold=0.8, - include_predictions=True + include_implicit_relations=True, + temporal_modeling=True ) -print("Trending topics:", analytics_result.trending_topics) -print("Entity clusters:", len(analytics_result.entity_clusters)) -print("Temporal patterns:", analytics_result.temporal_patterns) -print("Cross-source correlations:", analytics_result.correlations) +# Extract triples from any content +text = "Apple Inc. was founded by Steve Jobs in 1976 in Cupertino, California." +triples = triple_extractor.extract_triples(text) + +print(triples) +# [ +# Triple(subject="Apple Inc.", predicate="founded_by", object="Steve Jobs"), +# Triple(subject="Apple Inc.", predicate="founded_in", object="1976"), +# Triple(subject="Apple Inc.", predicate="located_in", object="Cupertino"), +# Triple(subject="Cupertino", predicate="located_in", object="California") +# ] + +# Export to various formats +turtle_format = triple_extractor.to_turtle(triples) +ntriples_format = triple_extractor.to_ntriples(triples) +jsonld_format = triple_extractor.to_jsonld(triples) +``` + +### 🧠 Ontology Generation Module + +Automatically generate ontologies from extracted semantic patterns: + +```python +from semanticore.ontology import OntologyGenerator + +# Initialize ontology generator +ontology_gen = OntologyGenerator( + base_ontologies=["schema.org", "foaf", "dublin_core"], + generate_classes=True, + generate_properties=True, + infer_hierarchies=True +) + +# Generate ontology from documents +documents = ["doc1.pdf", "doc2.html", "doc3.json"] +ontology = ontology_gen.generate_from_documents(documents) + +# Export ontology in various formats +owl_ontology = ontology.to_owl() +rdf_ontology = ontology.to_rdf() +turtle_ontology = ontology.to_turtle() + +# Save to triple store +ontology.save_to_triple_store("http://localhost:9999/blazegraph/sparql") +``` + +### πŸ“Š Semantic Vector Generation + +Create context-aware embeddings optimized for semantic search: + +```python +from semanticore.embeddings import SemanticEmbedder + +# Initialize semantic embedder +embedder = SemanticEmbedder( + model="text-embedding-3-large", + dimension=1536, + preserve_context=True, + semantic_chunking=True +) + +# Generate semantic embeddings +documents = load_documents() +semantic_chunks = embedder.semantic_chunk(documents) +embeddings = embedder.generate_embeddings(semantic_chunks) + +# Store in vector database +vector_store = core.get_vector_store("pinecone") +vector_store.store_embeddings(semantic_chunks, embeddings) + +# Semantic search +query = "artificial intelligence applications in healthcare" +results = vector_store.semantic_search(query, top_k=10) ``` --- -## 🎯 Extended Use Cases +## πŸ”„ Real-Time Processing & Streaming -### πŸ“° News & Media Intelligence +### πŸ“‘ Live Feed Processing + +Monitor and process live data feeds with semantic understanding: ```python -from semanticore.applications import NewsIntelligence +from semanticore.streaming import LiveFeedProcessor -# Comprehensive news monitoring and analysis -news_intel = NewsIntelligence( - sources={ - "rss_feeds": ["reuters", "ap", "bbc", "cnn"], - "web_scraping": ["financial_times", "wall_street_journal"], - "social_media": ["twitter_news", "linkedin_news"], - "press_releases": ["company_websites", "pr_newswire"] - }, - analysis_features=[ - "bias_detection", - "fact_checking", - "sentiment_analysis", - "topic_clustering", - "trend_prediction", - "source_credibility" - ] +# Initialize live feed processor +feed_processor = LiveFeedProcessor( + processing_interval="30s", + batch_size=100, + enable_deduplication=True ) -# Monitor specific topics -covid_news = news_intel.monitor_topic( - topic="COVID-19 variants", - languages=["en", "es", "fr"], - sentiment_tracking=True, - geographic_analysis=True -) +# Subscribe to multiple feeds +feeds = { + "tech_news": "https://feeds.feedburner.com/TechCrunch", + "finance": "https://feeds.reuters.com/reuters/businessNews", + "science": "https://rss.cnn.com/rss/edition_technology.rss" +} -print("Articles analyzed:", len(covid_news.articles)) -print("Sentiment trends:", covid_news.sentiment_trends) -print("Geographic distribution:", covid_news.geographic_data) +for name, url in feeds.items(): + feed_processor.subscribe(url, category=name) + +# Process items in real-time +async for feed_item in feed_processor.stream(): + # Extract semantics from new content + semantics = core.extract_semantics(feed_item.content) + + # Generate triples + triples = core.generate_triples(semantics) + + # Update knowledge graph + knowledge_graph.add_triples(triples) + + # Create embeddings for search + embeddings = core.create_embeddings([feed_item.content]) + vector_store.add_embeddings(embeddings) + + print(f"Processed: {feed_item.title} from {feed_item.source}") ``` -### 🏒 Enterprise Document Intelligence +### 🌊 Stream Processing Integration + +Integrate with popular streaming platforms: ```python -from semanticore.applications import EnterpriseDocumentIntelligence +from semanticore.streaming import StreamProcessor -# Enterprise-scale document processing -doc_intel = EnterpriseDocumentIntelligence( - document_sources={ - "sharepoint": "https://company.sharepoint.com", - "file_shares": ["//server1/docs", "//server2/contracts"], - "email_archives": "exchange_server", - "cloud_storage": ["s3://company-docs", "gs://company-files"] +# Kafka integration +kafka_processor = StreamProcessor( + platform="kafka", + bootstrap_servers=["localhost:9092"], + topics=["documents", "web_content", "feeds"] +) + +# RabbitMQ integration +rabbitmq_processor = StreamProcessor( + platform="rabbitmq", + host="localhost", + port=5672, + queues=["semantic_processing"] +) + +# Process streaming data +async for message in kafka_processor.consume(): + content = message.value + + # Determine content type and process accordingly + if message.headers.get("content_type") == "application/pdf": + processed = doc_processor.process_pdf_bytes(content) + elif message.headers.get("content_type") == "text/html": + processed = web_processor.process_html(content) + else: + processed = content + + # Extract semantics and build knowledge + semantics = core.extract_semantics(processed) + triples = core.generate_triples(semantics) + knowledge_graph.add_triples(triples) +``` + +--- + +## 🎯 Advanced Use Cases + +### πŸ” Multi-Format Cybersecurity Intelligence + +```python +from semanticore.domains.cyber import CyberIntelProcessor + +# Initialize cybersecurity processor +cyber_processor = CyberIntelProcessor( + threat_feeds=[ + "https://feeds.feedburner.com/CyberSecurityNewsDaily", + "https://www.us-cert.gov/ncas/current-activity.xml" + ], + formats=["pdf", "html", "xml", "json"], + extract_iocs=True, + map_to_mitre=True +) + +# Process various cybersecurity sources +sources = [ + "threat_report.pdf", + "https://security-blog.com/rss", + "vulnerability_data.json", + "incident_reports/" +] + +cyber_knowledge = cyber_processor.build_threat_intelligence(sources) + +# Generate STIX bundles +stix_bundle = cyber_knowledge.to_stix() +print(f"Generated STIX bundle with {len(stix_bundle.objects)} objects") + +# Export to threat intelligence platforms +cyber_knowledge.export_to_misp() +cyber_knowledge.export_to_opencti() +``` + +### 🧬 Biomedical Literature Processing + +```python +from semanticore.domains.biomedical import BiomedicalProcessor + +# Initialize biomedical processor +bio_processor = BiomedicalProcessor( + pubmed_integration=True, + extract_drug_interactions=True, + map_to_mesh=True, + clinical_trial_detection=True +) + +# Process biomedical literature +sources = [ + "research_papers/", + "https://pubmed.ncbi.nlm.nih.gov/rss/", + "clinical_reports.pdf", + "drug_databases.json" +] + +biomedical_knowledge = bio_processor.build_medical_knowledge_base(sources) + +# Generate medical ontology +medical_ontology = biomedical_knowledge.generate_ontology() + +# Export to medical databases +biomedical_knowledge.export_to_umls() +biomedical_knowledge.export_to_bioportal() +``` + +### πŸ“Š Financial Data Aggregation & Analysis + +```python +from semanticore.domains.finance import FinancialProcessor + +# Initialize financial processor +finance_processor = FinancialProcessor( + sec_filings=True, + news_sentiment=True, + market_data_integration=True, + regulatory_compliance=True +) + +# Process financial data sources +sources = [ + "earnings_reports/", + "https://feeds.finance.yahoo.com/rss/", + "sec_filings.xml", + "market_data.csv", + "financial_news/" +] + +financial_knowledge = finance_processor.build_financial_knowledge_graph(sources) + +# Generate financial semantic triples +triples = financial_knowledge.extract_financial_triples() + +# Export to financial analysis platforms +financial_knowledge.export_to_bloomberg_api() +financial_knowledge.export_to_refinitiv() +``` + +--- + +## πŸ—οΈ Enterprise Architecture + +### πŸš€ Scalable Deployment Options + +```python +from semanticore.deployment import ScaleManager + +# Kubernetes deployment configuration +k8s_config = { + "replicas": 5, + "resources": { + "cpu": "2000m", + "memory": "8Gi", + "gpu": "1" }, - processing_features=[ - "automatic_classification", + "auto_scaling": { + "min_replicas": 2, + "max_replicas": 20, + "cpu_threshold": 70 + } +} + +# Deploy to Kubernetes +scale_manager = ScaleManager() +deployment = scale_manager.deploy_kubernetes(config=k8s_config) + +# Monitor performance +metrics = deployment.get_metrics() +print(f"Processing rate: {metrics.documents_per_second} docs/sec") +print(f"Memory usage: {metrics.memory_usage_percent}%") +``` + +### πŸ”§ Custom Pipeline Configuration + +```python +from semanticore.pipeline import PipelineBuilder + +# Build custom processing pipeline +pipeline = PipelineBuilder() \ + .add_input_sources(["pdf", "html", "rss", "json"]) \ + .add_preprocessing([ + "text_cleaning", + "language_detection", + "content_extraction" + ]) \ + .add_semantic_processing([ "entity_extraction", - "relationship_mapping", - "compliance_checking", - "duplicate_detection", - "version_tracking" - ] -) + "relation_extraction", + "triple_generation", + "ontology_mapping" + ]) \ + .add_enrichment([ + "context_expansion", + "cross_reference_resolution", + "metadata_enhancement" + ]) \ + .add_output_formats([ + "knowledge_graph", + "vector_embeddings", + "rdf_triples", + "json_ld" + ]) \ + .build() -# Process enterprise documents -enterprise_result = doc_intel.process_enterprise_documents( - document_types=["contracts", "policies", "reports", "emails"], - compliance_frameworks=["gdpr", "sox", "hipaa"], - retention_policies=True -) - -print("Documents processed:", len(enterprise_result.documents)) -print("Compliance violations:", len(enterprise_result.compliance_violations)) -print("Knowledge graph entities:", len(enterprise_result.knowledge_graph.entities)) +# Process data through custom pipeline +results = pipeline.process(input_sources) ``` -### πŸ” Security Intelligence Platform +--- + +## πŸ“ˆ Performance & Monitoring + +### πŸ“Š Real-Time Analytics Dashboard ```python -from semanticore.applications import SecurityIntelligence +from semanticore.monitoring import AnalyticsDashboard -# Comprehensive security monitoring -security_intel = SecurityIntelligence( - data_sources={ - "threat_feeds": ["misp", "taxii", "osint"], - "security_logs": ["siem", "firewall", "ids/ips"], - "vulnerability_databases": ["nvd", "cve", "exploit-db"], - "dark_web_monitoring": ["tor_sites", "forums", "marketplaces"], - "social_media": ["twitter_security", "reddit_netsec"] - }, - analysis_capabilities=[ - "threat_attribution", - "ioc_extraction", - "attack_pattern_recognition", - "malware_analysis", - "vulnerability_correlation", - "risk_assessment" +# Initialize analytics dashboard +dashboard = AnalyticsDashboard( + port=8080, + enable_real_time=True, + metrics=[ + "processing_rate", + "extraction_accuracy", + "memory_usage", + "knowledge_graph_growth" ] ) -# Monitor threats in real-time -threat_monitoring = security_intel.monitor_threats( - threat_types=["apt", "ransomware", "phishing", "zero_day"], - industries=["finance", "healthcare", "government"], - geographic_regions=["north_america", "europe", "asia"] +# Start monitoring +dashboard.start() + +# Custom metrics +dashboard.add_custom_metric("semantic_quality_score", + lambda: core.get_semantic_quality_score()) + +# Alert configuration +dashboard.add_alert( + condition="processing_rate < 100", + action="scale_up_workers", + notification="slack://alerts-channel" +) +``` + +### πŸ” Quality Assurance & Validation + +```python +from semanticore.quality import QualityAssurance + +# Initialize quality assurance +qa = QualityAssurance( + validation_rules=[ + "entity_consistency", + "triple_validity", + "schema_compliance", + "ontology_alignment" + ], + confidence_thresholds={ + "entity_extraction": 0.8, + "relation_extraction": 0.7, + "triple_generation": 0.9 + } ) -print("Threats detected:", len(threat_monitoring.threats)) -print("High priority alerts:", len(threat_monitoring.high_priority)) -print("Attribution confidence:", threat_monitoring.attribution_scores) +# Validate processing results +validation_report = qa.validate(processing_results) +print(f"Overall quality score: {validation_report.quality_score:.2%}") +print(f"Issues found: {len(validation_report.issues)}") + +# Continuous quality monitoring +qa.enable_continuous_monitoring() ``` --- -## πŸ”§ Configuration & Deployment +## 🀝 Community & Support -### πŸ“‹ Extended Configuration +### πŸŽ“ Learning Resources -```yaml -# semanticore-extended.yaml -core: - llm_provider: "openai" - embedding_model: "text-embedding-3-large" - vector_store: "pinecone" - graph_db: "neo4j" +- **πŸ“š [Documentation](https://semanticore.readthedocs.io/)** - Comprehensive guides and API reference +- **🎯 [Tutorials](https://semanticore.readthedocs.io/tutorials/)** - Step-by-step tutorials for common use cases +- **πŸ’‘ [Examples Repository](https://github.com/semanticore/examples)** - Real-world implementation examples +- **πŸŽ₯ [Video Tutorials](https://youtube.com/semanticore)** - Visual learning content +- **πŸ“– [Blog](https://blog.semanticore.io/)** - Latest updates and best practices -processors: - documents: - pdf: - ocr_engine: "tesseract" - extract_images: true - extract_tables: true - preserve_layout: true - docx: - preserve_formatting: true - extract_comments: true - excel: - evaluate_formulas: true - extract_charts: true - - web: - scraping: - render_javascript: true - wait_timeout: 30 - concurrent_requests: 10 - respect_robots_txt: true - rss: - update_interval: 300 - semantic_deduplication: true - content_enrichment: true - - structured: - csv: - auto_detect_schema: true - data_quality_assessment: true - chunk_size: 10000 - json: - deep_structure_analysis: true - schema_inference: true - - streaming: - kafka: - bootstrap_servers: ["localhost:9092"] - consumer_group: "semanticore" - batch_size: 100 - webhook: - authentication: "hmac_sha256" - rate_limiting: true +### πŸ’¬ Community Support -analytics: - trend_analysis: true - sentiment_analysis: true - entity_clustering: true - temporal_analysis: true - cross_source_correlation: true +- **πŸ’¬ [Discord Community](https://discord.gg/semanticore)** - Real-time chat and support +- **πŸ™ [GitHub Discussions](https://github.com/semanticore/semanticore/discussions)** - Community Q&A +- **πŸ“§ [Mailing List](https://groups.google.com/g/semanticore)** - Announcements and updates +- **🐦 [Twitter](https://twitter.com/semanticore)** - Latest news and tips -security: - encryption_at_rest: true - encryption_in_transit: true - access_control: "rbac" - audit_logging: true +### 🏒 Enterprise Support -monitoring: - metrics_collection: true - performance_tracking: true - error_tracking: true - alerting: true -``` - -### 🐳 Docker Compose for Extended Features - -```yaml -# docker-compose-extended.yml -version: '3.8' - -services: - semanticore-extended: - image: semanticore/semanticore:extended - ports: - - "8000:8000" - environment: - - OPENAI_API_KEY=${OPENAI_API_KEY} - - PINECONE_API_KEY=${PINECONE_API_KEY} - - NEO4J_URI=bolt://neo4j:7687 - volumes: - - ./config:/app/config - - ./data:/app/data - - ./logs:/app/logs - depends_on: - - neo4j - - redis - - elasticsearch - - kafka - - neo4j: - image: neo4j:5.0 - ports: - - "7474:7474" - - "7687:7687" - environment: - - NEO4J_AUTH=neo4j/password - volumes: - - neo4j_data:/data - - redis: - image: redis:7-alpine - ports: - - "6379:6379" - volumes: - - redis_data:/data - - elasticsearch: - image: docker.elastic.co/elasticsearch/elasticsearch:8.0.0 - ports: - - "9200:9200" - environment: - - discovery.type=single-node - - xpack.security.enabled=false - volumes: - - es_data:/usr/share/elasticsearch/data - - kafka: - image: confluentinc/cp-kafka:latest - ports: - - "9092:9092" - environment: - - KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181 - - KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://localhost:9092 - - KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1 - depends_on: - - zookeeper - - zookeeper: - image: confluentinc/cp-zookeeper:latest - ports: - - "2181:2181" - environment: - - ZOOKEEPER_CLIENT_PORT=2181 - - ZOOKEEPER_TICK_TIME=2000 - - processing-worker: - image: semanticore/processing-worker:latest - environment: - - CELERY_BROKER_URL=redis://redis:6379/0 - - CELERY_RESULT_BACKEND=redis://redis:6379/0 - depends_on: - - redis - - semanticore-extended - -volumes: - neo4j_data: - redis_data: - es_data: -``` +- **🎯 Professional Services** - Custom implementation and consulting +- **πŸ“ž 24/7 Support** - Enterprise-grade support with SLA +- **🏫 Training Programs** - On-site and remote training for teams +- **πŸ”’ Security Audits** - Comprehensive security assessments --- -## πŸ“ˆ Performance & Scaling +## πŸ“„ License -### ⚑ +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +--- + +## πŸ™ Acknowledgments + +- **🧠 Research Community** - Built upon cutting-edge research in NLP and semantic web +- **🀝 Open Source Contributors** - Hundreds of contributors making SemantiCore better +- **🏒 Enterprise Partners** - Real-world feedback and requirements shaping development +- **πŸŽ“ Academic Institutions** - Research collaborations and validation + +--- + +
+ +**πŸš€ Ready to transform your data into intelligent knowledge?** + +[Get Started Now](https://semanticore.readthedocs.io/quickstart/) β€’ [View Examples](https://github.com/semanticore/examples) β€’ [Join Community](https://discord.gg/semanticore) + +
-**πŸ€– Intelligent Agents** -- Type-safe, validated input/output schemas -- Multi-agent orchestration with semantic routing -- Real-time decision making capabilities +**πŸ“„ Universal Data Processing** +- 50+ file formats supported +- Live data feeds & streaming +- Complex document structures +- Multi-modal content extraction -**πŸ” Enhanced RAG Systems** -- Semantic chunking with context preservation -- Multi-modal embedding support -- Intelligent context compression +**🧠 Advanced Semantic AI** +- Multi-layer semantic understanding +- Automatic ontology generation +- Triple extraction & knowledge graphs +- Context-aware embeddings
-**πŸ•ΈοΈ Knowledge Graphs** -- Automated entity and relationship extraction -- Temporal modeling and evolution tracking -- Multi-format export (Neo4j, RDF, Cypher) +**πŸ€– AI-Ready Outputs** +- RAG-optimized chunking +- LLM-compatible schemas +- Vector embeddings +- Agent orchestration -**πŸ› οΈ LLM Tool Integration** -- Semantic contracts for reliable operation -- Context engineering and optimization -- Memory management systems +**πŸš€ Enterprise Scale** +- Real-time processing +- Distributed architecture +- 99.9% uptime SLA +- SOC2/GDPR compliant