diff --git a/README.md b/README.md index f3fb1d32..0731e349 100644 --- a/README.md +++ b/README.md @@ -11,11 +11,11 @@ [![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€ Open Source Semantic Layer and Knowlege Engineering Toolkit** +**πŸš€ Open Source Semantic Layer and Knowledge Engineering Toolkit** *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/) +[πŸ“– Documentation](https://semanticore.readthedocs.io/) β€’ [πŸš€ Quick Start](#-quick-start) β€’ [πŸ’‘ Features](#-features) β€’ [🀝 Community](#-community--support) β€’ [πŸ”§ API Reference](https://semanticore.readthedocs.io/api/) @@ -74,61 +74,91 @@ SemantiCore is the most comprehensive semantic data transformation platform that --- -## ✨ Core Capabilities +## ✨ Features -### 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 +### πŸ“Š **Supported Data Formats** -### 🧠 **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 -- **Temporal Analysis**: Time-aware semantic understanding and event sequencing -- **Cross-Document Linking**: Entity resolution and relationship mapping across sources -- **Ontology Alignment**: Automatic mapping to existing ontologies (Schema.org, FOAF, Dublin Core) +| Category | Formats | Count | +|----------|---------|-------| +| **Document Formats** | PDF, DOCX, XLSX, PPTX, TXT, RTF, ODT, EPUB, LaTeX | 9 | +| **Web Formats** | HTML, XML, XHTML, RSS, Atom, JSON-LD, Sitemap XML | 7 | +| **Structured Data** | JSON, YAML, CSV, TSV, Parquet, Avro, ORC | 7 | +| **Markup Languages** | Markdown, ReStructuredText, AsciiDoc, Confluence | 4 | +| **Feed Processing** | RSS/Atom feeds, news feeds, social media streams | 3 | +| **Email Formats** | EML, MSG, MBOX, PST archives | 4 | +| **Archive Formats** | ZIP, TAR, RAR, 7Z with recursive processing | 4 | +| **Database Exports** | SQL dumps, MongoDB exports, CouchDB | 3 | +| **Scientific Formats** | BibTeX, EndNote, RIS, JATS XML | 4 | +| **Code Repositories** | Git repositories, documentation, README files | 3 | +| **Total Supported Formats** | | **50+** | -### πŸ•ΈοΈ **Knowledge Graph Construction** -- **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 -- **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 +### 🧠 **Semantic Processing Capabilities** -### 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 -- **Content Enrichment**: Automatic metadata extraction and enhancement -- **Cross-Reference Resolution**: Link resolution across documents and formats -- **Summarization**: Extractive and abstractive summarization with semantic preservation +| Feature | Description | Supported Models | +|---------|-------------|------------------| +| **Multi-layer Understanding** | Lexical, syntactic, semantic, and pragmatic analysis | Custom NLP pipelines | +| **Entity & Relationship Extraction** | Named entities, relationships, and complex event detection | spaCy, NLTK, Custom | +| **Automatic Triple Generation** | Subject-Predicate-Object triples from any content | RDF, JSON-LD, Custom | +| **Context Preservation** | Maintain semantic context across document boundaries | Advanced chunking | +| **Temporal Analysis** | Time-aware semantic understanding and event sequencing | Temporal reasoning | +| **Cross-Document Linking** | Entity resolution and relationship mapping across sources | Graph algorithms | +| **Ontology Alignment** | Automatic mapping to existing ontologies | Schema.org, FOAF, Dublin Core | -### 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•ΈοΈ **Knowledge Graph Features** + +| Feature | Description | Supported Backends | +|---------|-------------|-------------------| +| **Automated Construction** | Build knowledge graphs from any data format | All major graph DBs | +| **Triple Stores** | Blazegraph, Virtuoso, Apache Jena, GraphDB | 4+ engines | +| **Graph Databases** | Neo4j, KuzuDB, ArangoDB, Amazon Neptune, TigerGraph | 5+ databases | +| **Semantic Reasoning** | Inductive, deductive, and abductive reasoning | Custom reasoning engines | +| **Ontology Generation** | Automatic OWL/RDF ontology creation from data patterns | OWL 2.0, RDF 1.1 | +| **Graph Analytics** | Centrality analysis, community detection, path finding | NetworkX, Custom | +| **SPARQL Query Generation** | Automatic query generation for semantic search | SPARQL 1.1 | + +### πŸ“ˆ **Content Transformation Features** + +| Feature | Description | Output Formats | +|---------|-------------|----------------| +| **Semantic Chunking** | Context-aware document segmentation for RAG systems | JSON, CSV, Custom | +| **Multi-Modal Embeddings** | Text, image, table, and chart embeddings | OpenAI, Cohere, Custom | +| **Schema Evolution** | Dynamic schema adaptation and versioning | JSON Schema, XSD | +| **Content Enrichment** | Automatic metadata extraction and enhancement | Dublin Core, Custom | +| **Cross-Reference Resolution** | Link resolution across documents and formats | Graph links | +| **Summarization** | Extractive and abstractive summarization with semantic preservation | Text, JSON | + +### πŸ” **Text & Content Analysis** + +| Feature | Description | Supported Languages | +|---------|-------------|---------------------| +| **Topic Modeling** | LDA, BERTopic, hierarchical topic discovery | 100+ languages | +| **Sentiment Analysis** | Document, sentence, and aspect-level sentiment | Multi-language | +| **Language Detection** | 100+ languages with confidence scoring | 100+ languages | +| **Content Classification** | Automatic categorization and tagging | Custom taxonomies | +| **Duplicate Detection** | Semantic similarity and near-duplicate identification | Fuzzy matching | +| **Information Extraction** | Tables, figures, citations, references | Multi-format | ### 🌐 **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 + +| Feature | Description | Supported Platforms | +|---------|-------------|---------------------| +| **RSS/Atom Feed Monitoring** | Real-time feed processing and semantic extraction | All RSS/Atom feeds | +| **Web Scraping** | Intelligent web content extraction with semantic understanding | Any web content | +| **API Integration** | REST, GraphQL, WebSocket real-time data processing | Standard APIs | +| **Stream Processing** | Kafka, RabbitMQ, Pulsar integration | 3+ platforms | +| **Social Media Feeds** | Twitter, LinkedIn, Reddit semantic monitoring | 3+ platforms | +| **News Aggregation** | Multi-source news processing and semantic analysis | Global news sources | + +### 🏒 **Enterprise Features** + +| Feature | Description | Enterprise Tier | +|---------|-------------|-----------------| +| **Schema-First Construction** | Predefined business schemas with validation | Pro+ | +| **Seed-Based Initialization** | Start with known entities and enhance | Pro+ | +| **Duplicate Detection** | Automatic deduplication with business rules | Pro+ | +| **Conflict Detection** | Flag contradictions with source tracking | Pro+ | +| **Business Rules Engine** | Custom business logic and constraints | Enterprise | +| **Interactive Dashboard** | Built-in UI for conflict resolution | Enterprise | --- @@ -188,879 +218,102 @@ results = knowledge_base.query("What are the key financial trends?") --- -## πŸ”§ Data Processing Modules - -### πŸ“„ Document Processing Module - -Process complex document formats with semantic understanding: - -```python -from semanticore.processors import DocumentProcessor - -# Initialize document processor -doc_processor = DocumentProcessor( - extract_tables=True, - extract_images=True, - extract_metadata=True, - preserve_structure=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") - -# 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 & Feed Processing Module - -Real-time web content and feed processing: - -```python -from semanticore.processors import WebProcessor, FeedProcessor - -# Web content processor -web_processor = WebProcessor( - respect_robots=True, - extract_metadata=True, - follow_redirects=True, - max_depth=3 -) - -# RSS/Atom feed processor -feed_processor = FeedProcessor( - update_interval="5m", - deduplicate=True, - extract_full_content=True -) - -# Process web content -webpage = web_processor.process_url("https://example.com/article") -semantics = core.extract_semantics(webpage.content) - -# Monitor RSS feeds -feeds = [ - "https://feeds.feedburner.com/TechCrunch", - "https://rss.cnn.com/rss/edition.rss", - "https://feeds.reuters.com/reuters/topNews" -] - -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 Module - -Handle structured and semi-structured data formats: - -```python -from semanticore.processors import StructuredDataProcessor - -# Initialize structured data processor -structured_processor = StructuredDataProcessor( - infer_schema=True, - extract_relationships=True, - generate_ontology=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") - -# 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) -``` - -### πŸ“§ Email & Archive Processing Module - -Process email archives and compressed files: - -```python -from semanticore.processors import EmailProcessor, ArchiveProcessor - -# Email processing -email_processor = EmailProcessor( - extract_attachments=True, - parse_headers=True, - thread_detection=True -) - -# Archive processing -archive_processor = ArchiveProcessor( - recursive=True, - supported_formats=['zip', 'tar', 'rar', '7z'], - max_depth=5 -) - -# Process email archives -mbox_data = email_processor.process_mbox("emails.mbox") -pst_data = email_processor.process_pst("outlook.pst") - -# Process compressed archives -archive_contents = archive_processor.process_archive("documents.zip") - -# Extract semantic information from all contents -for content in archive_contents: - semantics = core.extract_semantics(content) - triples = core.generate_triples(semantics) -``` - -### πŸ”¬ Scientific & Academic Processing Module - -Specialized processing for academic and scientific content: - -```python -from semanticore.processors import AcademicProcessor - -# Academic content processor -academic_processor = AcademicProcessor( - extract_citations=True, - parse_references=True, - identify_sections=True, - extract_figures=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") - -# 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) -``` - ---- - -## 🧩 Semantic Extraction & Transformation - -### 🎯 Automatic Triple Generation - -Generate RDF triples from any content automatically: - -```python -from semanticore.extraction import TripleExtractor - -# Initialize triple extractor -triple_extractor = TripleExtractor( - confidence_threshold=0.8, - include_implicit_relations=True, - temporal_modeling=True -) - -# 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) -``` - ---- - -## πŸ”„ Real-Time Processing & Streaming - -### πŸ“‘ Live Feed Processing - -Monitor and process live data feeds with semantic understanding: - -```python -from semanticore.streaming import LiveFeedProcessor - -# Initialize live feed processor -feed_processor = LiveFeedProcessor( - processing_interval="30s", - batch_size=100, - enable_deduplication=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" -} - -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}") -``` - -### 🌊 Stream Processing Integration - -Integrate with popular streaming platforms: - -```python -from semanticore.streaming import StreamProcessor - -# 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) -``` +## πŸ”§ Core Modules + +### πŸ“„ **Document Processing Module** +- **Supported Formats**: PDF, DOCX, XLSX, PPTX, TXT, RTF, ODT, EPUB, LaTeX +- **Features**: Table extraction, image processing, metadata extraction, structure preservation +- **Use Cases**: Document analysis, content extraction, structured data conversion + +### 🌐 **Web & Feed Processing Module** +- **Supported Formats**: HTML, XML, RSS, Atom, JSON-LD, Sitemaps +- **Features**: Real-time monitoring, content extraction, metadata parsing +- **Use Cases**: Web scraping, feed aggregation, content monitoring + +### πŸ“Š **Structured Data Processing Module** +- **Supported Formats**: JSON, YAML, CSV, TSV, Parquet, Avro, ORC +- **Features**: Schema inference, relationship extraction, ontology generation +- **Use Cases**: Data integration, schema mapping, knowledge extraction + +### πŸ“§ **Email & Archive Processing Module** +- **Supported Formats**: EML, MSG, MBOX, PST, ZIP, TAR, RAR, 7Z +- **Features**: Attachment processing, thread detection, recursive extraction +- **Use Cases**: Email analysis, archive processing, content discovery + +### πŸ”¬ **Scientific & Academic Processing Module** +- **Supported Formats**: LaTeX, BibTeX, EndNote, RIS, JATS XML +- **Features**: Citation extraction, reference parsing, figure identification +- **Use Cases**: Research analysis, academic content processing, literature review --- ## 🎯 Advanced Use Cases -### πŸ” Multi-Format Cybersecurity Intelligence +### πŸ” **Multi-Format Cybersecurity Intelligence** +- **Data Sources**: Threat reports, security blogs, vulnerability databases, incident reports +- **Outputs**: STIX bundles, MISP integration, OpenCTI export +- **Features**: IOC extraction, MITRE ATT&CK mapping, threat intelligence -```python -from semanticore.domains.cyber import CyberIntelProcessor +### 🧬 **Biomedical Literature Processing** +- **Data Sources**: Research papers, PubMed feeds, clinical reports, drug databases +- **Outputs**: Medical ontologies, UMLS integration, BioPortal export +- **Features**: Drug interaction detection, MeSH mapping, clinical trial analysis -# 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“Š **Financial Data Aggregation & Analysis** +- **Data Sources**: SEC filings, financial news, market data, earnings reports +- **Outputs**: Financial knowledge graphs, Bloomberg API, Refinitiv export +- **Features**: News sentiment analysis, regulatory compliance, market intelligence --- ## πŸ—οΈ Enterprise Architecture -### πŸš€ Scalable Deployment Options +### πŸš€ **Scalable Deployment Options** +- **Kubernetes**: Auto-scaling, resource management, high availability +- **Docker**: Containerized deployment, easy scaling, portability +- **Cloud Native**: AWS, Azure, GCP integration with managed services +- **On-Premise**: Self-hosted solutions with enterprise security -```python -from semanticore.deployment import ScaleManager - -# Kubernetes deployment configuration -k8s_config = { - "replicas": 5, - "resources": { - "cpu": "2000m", - "memory": "8Gi", - "gpu": "1" - }, - "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", - "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 data through custom pipeline -results = pipeline.process(input_sources) -``` +### πŸ”§ **Custom Pipeline Configuration** +- **Modular Design**: Mix and match processing components +- **Custom Rules**: Business logic and validation engines +- **Quality Control**: Built-in validation and conflict detection +- **Monitoring**: Real-time analytics and performance dashboards --- ## πŸ“ˆ Performance & Monitoring -### πŸ“Š Real-Time Analytics Dashboard - -```python -from semanticore.monitoring import AnalyticsDashboard - -# Initialize analytics dashboard -dashboard = AnalyticsDashboard( - port=8080, - enable_real_time=True, - metrics=[ - "processing_rate", - "extraction_accuracy", - "memory_usage", - "knowledge_graph_growth" - ] -) - -# 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 - } -) - -# 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() -``` - - -## 🏒 Enterprise Knowledge Graph Features - -### πŸ“‹ Schema-First Knowledge Graph Construction - -Unlike other libraries that infer schemas, SemantiCore enforces predefined business schemas: - -```python -from semanticore.schema import SchemaManager, BusinessEntity -from pydantic import BaseModel -from typing import List, Optional - -# Define your business schema upfront -class Employee(BusinessEntity): - name: str - employee_id: str - department: str - role: str - manager: Optional[str] = None - email: str - hire_date: str - -class Department(BusinessEntity): - name: str - budget: float - head: str - location: str - -class Product(BusinessEntity): - name: str - sku: str - department: str - owner: str - price: float - launch_date: str - -# Initialize schema manager with your business entities -schema_manager = SchemaManager() -schema_manager.register_entities([Employee, Department, Product]) - -# Process documents with schema enforcement -core = SemantiCore(schema_manager=schema_manager) -results = core.process_with_schema("hr_documents/", strict_mode=True) - -# Only entities matching your schema are extracted and validated -print(f"Extracted {len(results.employees)} employees") -print(f"Extracted {len(results.departments)} departments") -print(f"Schema violations: {len(results.violations)}") -``` - -### 🌱 Seed-Based Knowledge Graph Initialization - -Start with known entities and enhance with automated extraction: - -```python -from semanticore.knowledge import SeedManager - -# Initialize with known business entities -seed_manager = SeedManager() - -# Load seed data from various sources -seed_manager.load_from_csv("employees.csv", entity_type="Employee") -seed_manager.load_from_json("departments.json", entity_type="Department") -seed_manager.load_from_database("products", connection_string="postgresql://...") - -# Seed the knowledge graph -knowledge_graph = core.create_knowledge_graph(seed_data=seed_manager.get_seeds()) - -# Process new documents - will match against seeded entities -new_documents = ["meeting_notes.pdf", "project_reports/", "emails.mbox"] -results = core.process_documents(new_documents, - knowledge_graph=knowledge_graph, - enable_entity_linking=True) - -# Results show both seeded and newly discovered entities -print(f"Seeded entities: {len(knowledge_graph.seeded_entities)}") -print(f"Newly discovered: {len(results.new_entities)}") -print(f"Linked to existing: {len(results.linked_entities)}") -``` - -### πŸ”„ Intelligent Duplicate Detection & Merging - -Automatic deduplication with configurable business rules: - -```python -from semanticore.deduplication import EntityDeduplicator - -# Configure deduplication rules for each entity type -dedup_config = { - "Employee": { - "match_fields": ["email", "employee_id"], - "fuzzy_fields": ["name"], - "similarity_threshold": 0.85, - "merge_strategy": "most_recent" - }, - "Product": { - "match_fields": ["sku"], - "fuzzy_fields": ["name"], - "similarity_threshold": 0.90, - "merge_strategy": "highest_confidence" - }, - "Department": { - "match_fields": ["name"], - "similarity_threshold": 0.95, - "merge_strategy": "manual_review" - } -} - -# Initialize deduplicator -deduplicator = EntityDeduplicator(config=dedup_config) - -# Process documents with automatic deduplication -results = core.process_documents( - sources=["hr_data/", "finance_reports/", "project_docs/"], - deduplicator=deduplicator, - enable_auto_merge=True -) - -# Review deduplication results -print(f"Duplicates found: {len(results.duplicates)}") -print(f"Auto-merged: {len(results.auto_merged)}") -print(f"Requires manual review: {len(results.manual_review_needed)}") - -# Access detailed merge information -for merge in results.auto_merged: - print(f"Merged {merge.entity_type}: {merge.canonical_name}") - print(f" Sources: {', '.join(merge.source_documents)}") - print(f" Confidence: {merge.confidence:.2%}") -``` - -### ⚠️ Conflict Detection & Source Traceability - -Flag contradictions with complete source tracking: - -```python -from semanticore.conflicts import ConflictDetector - -# Configure conflict detection rules -conflict_detector = ConflictDetector( - track_provenance=True, - conflict_fields={ - "Employee": ["salary", "department", "role", "manager"], - "Product": ["price", "owner", "department"], - "Department": ["budget", "head", "location"] - }, - confidence_threshold=0.7 -) - -# Process with conflict detection enabled -results = core.process_documents( - sources=["q1_report.pdf", "hr_database.csv", "manager_updates.docx"], - conflict_detector=conflict_detector -) - -# Review detected conflicts -for conflict in results.conflicts: - print(f"\n🚨 CONFLICT DETECTED: {conflict.entity_name}") - print(f"Field: {conflict.field}") - print(f"Conflicting values:") - - for claim in conflict.claims: - print(f" β€’ '{claim.value}' from {claim.source_document}") - print(f" Page: {claim.page_number}, Confidence: {claim.confidence:.2%}") - print(f" Context: {claim.context}") - - print(f"Recommended action: {conflict.recommended_action}") - -# Export conflicts for manual resolution -conflict_report = results.export_conflicts_report() -conflict_report.save_to_excel("conflicts_review.xlsx") - -# Resolve conflicts programmatically or through UI -resolution_rules = { - "Employee.salary": "use_most_recent", - "Product.price": "use_highest_confidence", - "Department.budget": "require_manual_review" -} - -resolved_conflicts = conflict_detector.resolve_conflicts( - results.conflicts, - rules=resolution_rules -) -``` - -### πŸ“Š Business Rules & Validation Engine - -Implement custom business logic and constraints: - -```python -from semanticore.validation import BusinessRuleEngine - -# Define business rules -rules = BusinessRuleEngine() - -# Add validation rules -rules.add_rule( - name="employee_department_exists", - condition="Employee.department must exist in Department entities", - severity="error" -) - -rules.add_rule( - name="salary_range_check", - condition="Employee.salary must be between $30,000 and $500,000", - severity="warning" -) - -rules.add_rule( - name="product_owner_validation", - condition="Product.owner must be an existing Employee", - severity="error" -) - -rules.add_rule( - name="department_budget_consistency", - condition="Department.budget should align with sum of employee salaries", - severity="info" -) - -# Process with business rule validation -results = core.process_documents( - sources=["company_data/"], - validation_engine=rules, - fail_on_errors=False -) - -# Review validation results -validation_report = results.validation_report - -print(f"Total violations: {len(validation_report.violations)}") -print(f"Errors: {validation_report.errors}") -print(f"Warnings: {validation_report.warnings}") -print(f"Info: {validation_report.info}") - -# Get detailed violation information -for violation in validation_report.violations: - print(f"\n❌ {violation.rule_name}") - print(f"Entity: {violation.entity_name} ({violation.entity_type})") - print(f"Issue: {violation.description}") - print(f"Source: {violation.source_document}") - print(f"Suggested fix: {violation.suggested_resolution}") -``` - -### 🎯 Interactive Conflict Resolution Dashboard - -Built-in UI for reviewing and resolving conflicts: - -```python -from semanticore.ui import ConflictResolutionDashboard - -# Start interactive dashboard -dashboard = ConflictResolutionDashboard( - knowledge_graph=knowledge_graph, - conflicts=results.conflicts, - port=8080 -) - -# Dashboard features: -# - Side-by-side source comparison -# - Confidence score visualization -# - One-click conflict resolution -# - Bulk resolution with rules -# - Export resolved data - -dashboard.start() -print("Dashboard available at http://localhost:8080") - -# Programmatic resolution after dashboard review -resolved_data = dashboard.get_resolved_conflicts() -knowledge_graph.apply_resolutions(resolved_data) -``` - -## 🎯 Advanced Use Cases - -### πŸ” Multi-Format Cybersecurity Intelligence +### πŸ“Š **Real-Time Analytics Dashboard** +- **Metrics**: Processing rate, extraction accuracy, memory usage, knowledge graph growth +- **Alerts**: Configurable thresholds and notifications +- **Visualization**: Interactive charts and performance graphs +- **Integration**: Slack, email, webhook notifications + +### πŸ” **Quality Assurance & Validation** +- **Validation Rules**: Entity consistency, triple validity, schema compliance +- **Confidence Scoring**: Configurable thresholds for extraction quality +- **Continuous Monitoring**: Real-time quality assessment +- **Issue Resolution**: Automated problem detection and resolution --- ## 🀝 Community & Support -### πŸŽ“ Learning 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