semantica/Modules.md

🧩 Semantica Modules & Submodules

Complete reference guide for all Semantica toolkit modules with practical code examples

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📋 Table of Contents

1. Core Modules
2. Data Processing
3. Semantic Intelligence
4. Storage & Retrieval
5. AI & Reasoning
6. Domain Specialization
7. User Interface
8. Operations
9. Complete Module Index
10. Import Reference

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🏗️ Core Modules

1. Core Engine (semantica.core)

Main Class: Semantica

Purpose: Central orchestration, configuration, and pipeline management

Imports:

from semantica import Semantica
from semantica.core import Config, PluginManager, Orchestrator, LifecycleManager
from semantica.core.orchestrator import PipelineCoordinator, TaskScheduler, ResourceManager
from semantica.core.config_manager import YAMLConfigParser, JSONConfigParser, EnvironmentConfig
from semantica.core.plugin_registry import PluginLoader, VersionCompatibility, DependencyResolver
from semantica.core.lifecycle import StartupHooks, ShutdownHooks, HealthChecker, GracefulDegradation

Main Functions:

# Initialize Semantica with configuration
core = Semantica(
    llm_provider="openai",
    embedding_model="text-embedding-3-large",
    vector_store="pinecone",
    graph_db="neo4j"
)

# Core functionality
core.initialize()                    # Setup all modules
knowledge_base = core.build_knowledge_base(sources)  # Process data
status = core.get_status()           # Get system health
pipeline = core.create_pipeline()    # Create processing pipeline
config = core.get_config()          # Get current configuration
plugins = core.list_plugins()       # List available plugins

Submodules with Functions:

Orchestrator (semantica.core.orchestrator):

from semantica.core.orchestrator import PipelineCoordinator, TaskScheduler, ResourceManager

# Pipeline coordination
coordinator = PipelineCoordinator()
coordinator.schedule_pipeline(pipeline_config)
coordinator.monitor_progress(pipeline_id)
coordinator.handle_failures(pipeline_id)

# Task scheduling
scheduler = TaskScheduler()
scheduler.schedule_task(task, priority="high")
scheduler.get_queue_status()
scheduler.cancel_task(task_id)

# Resource management
resource_manager = ResourceManager()
resource_manager.allocate_resources(requirements)
resource_manager.monitor_usage()
resource_manager.release_resources(resource_id)

Config Manager (semantica.core.config_manager):

from semantica.core.config_manager import YAMLConfigParser, JSONConfigParser, EnvironmentConfig

# YAML configuration
yaml_parser = YAMLConfigParser()
config = yaml_parser.load("config.yaml")
yaml_parser.validate(config, schema="config_schema.yaml")
yaml_parser.save(config, "output.yaml")

# JSON configuration
json_parser = JSONConfigParser()
config = json_parser.load("config.json")
json_parser.merge_configs(base_config, override_config)

# Environment configuration
env_config = EnvironmentConfig()
env_config.load_from_env()
env_config.set_defaults(defaults)

Plugin Registry (semantica.core.plugin_registry):

from semantica.core.plugin_registry import PluginLoader, VersionCompatibility, DependencyResolver

# Plugin loading
loader = PluginLoader()
plugin = loader.load_plugin("custom_processor", version="1.2.0")
loader.register_plugin(plugin)
loader.unload_plugin(plugin_id)

# Version compatibility
version_checker = VersionCompatibility()
compatible = version_checker.check_compatibility(plugin, semantica_version)
version_checker.get_compatible_versions(plugin_name)

# Dependency resolution
resolver = DependencyResolver()
dependencies = resolver.resolve_dependencies(plugin)
resolver.install_dependencies(dependencies)

Lifecycle (semantica.core.lifecycle):

from semantica.core.lifecycle import StartupHooks, ShutdownHooks, HealthChecker, GracefulDegradation

# Startup hooks
startup = StartupHooks()
startup.register_hook("database_init", init_database)
startup.register_hook("cache_warmup", warmup_cache)
startup.execute_hooks()

# Shutdown hooks
shutdown = ShutdownHooks()
shutdown.register_hook("cleanup_temp", cleanup_temp_files)
shutdown.register_hook("close_connections", close_db_connections)
shutdown.execute_hooks()

# Health checking
health = HealthChecker()
health.add_check("database", check_database_health)
health.add_check("memory", check_memory_usage)
status = health.run_checks()

# Graceful degradation
degradation = GracefulDegradation()
degradation.set_fallback_strategy("cache_only")
degradation.handle_service_failure(service_name)

2. Pipeline Builder (semantica.pipeline)

Main Class: PipelineBuilder

Purpose: Create and manage data processing pipelines

Imports:

from semantica.pipeline import PipelineBuilder, ExecutionEngine, FailureHandler
from semantica.pipeline.execution_engine import PipelineRunner, StepOrchestrator, ProgressTracker
from semantica.pipeline.failure_handler import RetryHandler, FallbackHandler, ErrorRecovery
from semantica.pipeline.parallelism_manager import ParallelExecutor, LoadBalancer, TaskDistributor
from semantica.pipeline.resource_scheduler import CPUScheduler, GPUScheduler, MemoryManager
from semantica.pipeline.pipeline_validator import DependencyChecker, CycleDetector, ConfigValidator
from semantica.pipeline.monitoring_hooks import MetricsCollector, AlertManager, StatusReporter
from semantica.pipeline.pipeline_templates import PrebuiltTemplates, CustomTemplates, TemplateManager

Main Functions:

# Build custom pipeline
pipeline = PipelineBuilder() \
    .add_step("ingest", {"source": "documents/"}) \
    .add_step("parse", {"formats": ["pdf", "docx"]}) \
    .add_step("extract", {"entities": True, "relations": True}) \
    .add_step("embed", {"model": "text-embedding-3-large"}) \
    .set_parallelism(4) \
    .build()

# Execute pipeline
results = pipeline.run()
pipeline.pause()                    # Pause execution
pipeline.resume()                   # Resume execution
pipeline.stop()                     # Stop execution
status = pipeline.get_status()      # Get current status

Submodules with Functions:

Execution Engine (semantica.pipeline.execution_engine):

from semantica.pipeline.execution_engine import PipelineRunner, StepOrchestrator, ProgressTracker

# Pipeline execution
runner = PipelineRunner()
runner.execute_pipeline(pipeline_config)
runner.pause_pipeline(pipeline_id)
runner.resume_pipeline(pipeline_id)
runner.stop_pipeline(pipeline_id)

# Step orchestration
orchestrator = StepOrchestrator()
orchestrator.coordinate_steps(steps)
orchestrator.manage_dependencies(step_dependencies)
orchestrator.handle_step_completion(step_id, result)

# Progress tracking
tracker = ProgressTracker()
tracker.track_progress(pipeline_id)
tracker.get_completion_percentage()
tracker.estimate_remaining_time()

Failure Handler (semantica.pipeline.failure_handler):

from semantica.pipeline.failure_handler import RetryHandler, FallbackHandler, ErrorRecovery

# Retry logic
retry_handler = RetryHandler(max_retries=3, backoff_factor=2.0)
retry_handler.retry_failed_step(step_id, error)
retry_handler.set_retry_policy(step_type, retry_policy)

# Fallback strategies
fallback = FallbackHandler()
fallback.set_fallback_strategy("cache_only")
fallback.handle_service_failure(service_name)
fallback.switch_to_backup(primary_failed)

# Error recovery
recovery = ErrorRecovery()
recovery.analyze_error(error)
recovery.suggest_recovery_actions(error)
recovery.execute_recovery(recovery_plan)

Parallelism Manager (semantica.pipeline.parallelism_manager):

from semantica.pipeline.parallelism_manager import ParallelExecutor, LoadBalancer, TaskDistributor

# Parallel execution
executor = ParallelExecutor(max_workers=8)
executor.execute_parallel(tasks)
executor.set_parallelism_level(level=4)
executor.monitor_worker_health()

# Load balancing
balancer = LoadBalancer()
balancer.distribute_load(tasks, workers)
balancer.rebalance_workload()
balancer.get_worker_utilization()

# Task distribution
distributor = TaskDistributor()
distributor.distribute_tasks(tasks, workers)
distributor.collect_results(worker_results)
distributor.handle_worker_failure(worker_id)

Resource Scheduler (semantica.pipeline.resource_scheduler):

from semantica.pipeline.resource_scheduler import CPUScheduler, GPUScheduler, MemoryManager

# CPU scheduling
cpu_scheduler = CPUScheduler()
cpu_scheduler.allocate_cpu(cores=4)
cpu_scheduler.set_cpu_affinity(process_id, cores)
cpu_scheduler.monitor_cpu_usage()

# GPU scheduling
gpu_scheduler = GPUScheduler()
gpu_scheduler.allocate_gpu(device_id=0)
gpu_scheduler.set_gpu_memory_limit(limit="8GB")
gpu_scheduler.monitor_gpu_usage()

# Memory management
memory_manager = MemoryManager()
memory_manager.allocate_memory(size="2GB")
memory_manager.optimize_memory_usage()
memory_manager.garbage_collect()

Pipeline Validator (semantica.pipeline.pipeline_validator):

from semantica.pipeline.pipeline_validator import DependencyChecker, CycleDetector, ConfigValidator

# Dependency checking
dep_checker = DependencyChecker()
dep_checker.check_dependencies(pipeline_steps)
dep_checker.validate_dependency_graph(graph)
dep_checker.suggest_dependency_fixes(issues)

# Cycle detection
cycle_detector = CycleDetector()
has_cycles = cycle_detector.detect_cycles(pipeline_graph)
cycles = cycle_detector.find_cycles(pipeline_graph)
cycle_detector.suggest_cycle_breaks(cycles)

# Configuration validation
config_validator = ConfigValidator()
config_validator.validate_config(pipeline_config)
config_validator.check_required_fields(config)
config_validator.validate_data_types(config)

Monitoring Hooks (semantica.pipeline.monitoring_hooks):

from semantica.pipeline.monitoring_hooks import MetricsCollector, AlertManager, StatusReporter

# Metrics collection
metrics = MetricsCollector()
metrics.collect_pipeline_metrics(pipeline_id)
metrics.record_step_duration(step_id, duration)
metrics.record_memory_usage(step_id, memory)

# Alert management
alerts = AlertManager()
alerts.set_alert_threshold("memory_usage", threshold=0.9)
alerts.send_alert("High memory usage detected")
alerts.configure_notifications(email="admin@example.com")

# Status reporting
reporter = StatusReporter()
reporter.generate_status_report(pipeline_id)
reporter.export_metrics(format="json")
reporter.create_dashboard_data()

Pipeline Templates (semantica.pipeline.pipeline_templates):

from semantica.pipeline.pipeline_templates import PrebuiltTemplates, CustomTemplates, TemplateManager

# Prebuilt templates
templates = PrebuiltTemplates()
doc_processing = templates.get_template("document_processing")
web_scraping = templates.get_template("web_scraping")
knowledge_extraction = templates.get_template("knowledge_extraction")

# Custom templates
custom = CustomTemplates()
custom.create_template("my_pipeline", steps)
custom.save_template(template, "my_pipeline.json")
custom.load_template("my_pipeline.json")

# Template management
manager = TemplateManager()
manager.list_templates()
manager.validate_template(template)
manager.export_template(template_id, "export.json")

---

📊 Data Processing

3. Data Ingestion (semantica.ingest)

Main Classes: FileIngestor, WebIngestor, FeedIngestor

Purpose: Ingest data from various sources

from semantica.ingest import FileIngestor, WebIngestor, FeedIngestor

# File ingestion
file_ingestor = FileIngestor()
files = file_ingestor.scan_directory("documents/", recursive=True)
formats = file_ingestor.detect_format("document.pdf")

# Web ingestion
web_ingestor = WebIngestor(respect_robots=True, max_depth=3)
web_content = web_ingestor.crawl_site("https://example.com")
links = web_ingestor.extract_links(web_content)

# Feed ingestion
feed_ingestor = FeedIngestor()
rss_data = feed_ingestor.parse_rss("https://example.com/feed.xml")

Submodules:

• file - Local files, cloud storage (S3, GCS, Azure)
• web - HTTP scraping, sitemap crawling, JavaScript rendering
• feed - RSS/Atom feeds, social media APIs
• stream - Real-time streams, WebSocket, message queues
• repo - Git repositories, package managers
• email - IMAP/POP3, Exchange, Gmail API
• db_export - Database dumps, SQL queries, ETL

4. Document Parsing (semantica.parse)

Main Classes: PDFParser, DOCXParser, HTMLParser, ImageParser

Purpose: Extract content from various document formats

from semantica.parse import PDFParser, DOCXParser, HTMLParser, ImageParser

# PDF parsing
pdf_parser = PDFParser()
pdf_text = pdf_parser.extract_text("document.pdf")
pdf_tables = pdf_parser.extract_tables("document.pdf")
pdf_images = pdf_parser.extract_images("document.pdf")

# DOCX parsing
docx_parser = DOCXParser()
docx_content = docx_parser.get_document_structure("document.docx")
track_changes = docx_parser.extract_track_changes("document.docx")

# HTML parsing
html_parser = HTMLParser()
dom_tree = html_parser.parse_dom("https://example.com")
metadata = html_parser.extract_metadata(dom_tree)

# Image parsing (OCR)
image_parser = ImageParser()
ocr_text = image_parser.ocr_text("image.png")
objects = image_parser.detect_objects("image.jpg")

Submodules:

• pdf - PDF text, tables, images, annotations
• docx - Word documents, styles, track changes
• pptx - PowerPoint slides, speaker notes
• excel - Spreadsheets, formulas, charts
• html - Web pages, DOM structure, metadata
• images - OCR, object detection, EXIF data
• tables - Table structure detection and extraction

5. Text Normalization (semantica.normalize)

Main Classes: TextCleaner, LanguageDetector, EntityNormalizer

Purpose: Clean and normalize text data

from semantica.normalize import TextCleaner, LanguageDetector, EntityNormalizer

# Text cleaning
cleaner = TextCleaner()
clean_text = cleaner.remove_html(html_content)
normalized = cleaner.normalize_whitespace(text)
cleaned = cleaner.remove_special_chars(text)

# Language detection
detector = LanguageDetector()
language = detector.detect("Hello world")
confidence = detector.get_confidence()
supported = detector.supported_languages()

# Entity normalization
normalizer = EntityNormalizer()
canonical = normalizer.canonicalize("Apple Inc.", "Apple")
expanded = normalizer.expand_acronyms("NASA")

Submodules:

• text_cleaner - HTML removal, whitespace normalization
• language_detector - Multi-language identification
• encoding_handler - UTF-8 conversion, encoding validation
• entity_normalizer - Named entity standardization
• date_normalizer - Date format standardization
• number_normalizer - Number format standardization

6. Text Chunking (semantica.split)

Main Classes: SemanticChunker, StructuralChunker, TableChunker

Purpose: Split documents into optimal chunks for processing

from semantica.split import SemanticChunker, StructuralChunker, TableChunker

# Semantic chunking
semantic_chunker = SemanticChunker()
chunks = semantic_chunker.split_by_meaning(long_text)
topics = semantic_chunker.detect_topics(text)

# Structural chunking
structural_chunker = StructuralChunker()
sections = structural_chunker.split_by_sections(document)
headers = structural_chunker.identify_headers(document)

# Table-aware chunking
table_chunker = TableChunker()
table_chunks = table_chunker.preserve_tables(document)
context = table_chunker.extract_table_context(table)

Submodules:

• sliding_window - Fixed-size chunks with overlap
• semantic_chunker - Meaning-based splitting
• structural_chunker - Document-aware splitting
• table_chunker - Table-preserving splitting
• provenance_tracker - Source tracking for chunks

---

🧠 Semantic Intelligence

7. Semantic Extraction (semantica.semantic_extract)

Main Classes: NERExtractor, RelationExtractor, TripleExtractor

Purpose: Extract semantic information from text

from semantica.semantic_extract import NERExtractor, RelationExtractor, TripleExtractor

# Named Entity Recognition
ner = NERExtractor()
entities = ner.extract_entities("Apple Inc. was founded by Steve Jobs in 1976")
classified = ner.classify_entities(entities)

# Relation Extraction
rel_extractor = RelationExtractor()
relations = rel_extractor.find_relations("Apple Inc. was founded by Steve Jobs")
classified_rels = rel_extractor.classify_relations(relations)

# Triple Extraction
triple_extractor = TripleExtractor()
triples = triple_extractor.extract_triples(text)
validated = triple_extractor.validate_triples(triples)

# Export triples
turtle = triple_extractor.to_turtle(triples)
jsonld = triple_extractor.to_jsonld(triples)

Submodules:

• ner_extractor - Named entity recognition and classification
• relation_extractor - Relationship detection and classification
• event_detector - Event identification and temporal extraction
• coref_resolver - Co-reference resolution and entity linking
• triple_extractor - RDF triple extraction
• llm_enhancer - LLM-based complex extraction

8. Ontology Generation (semantica.ontology)

Main Class: OntologyGenerator

Purpose: Generate ontologies from extracted data

from semantica.ontology import OntologyGenerator

# Initialize ontology generator
ontology_gen = OntologyGenerator(
    base_ontologies=["schema.org", "foaf", "dublin_core"],
    generate_classes=True,
    generate_properties=True
)

# Generate ontology from documents
ontology = ontology_gen.generate_from_documents(documents)

# Export 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")

Submodules:

• class_inferrer - Automatic class discovery and hierarchy
• property_generator - Property inference and data types
• owl_generator - OWL/RDF generation and serialization
• base_mapper - Schema.org, FOAF, Dublin Core mapping
• version_manager - Ontology versioning and migration

9. Knowledge Graph (semantica.kg)

Main Classes: GraphBuilder, EntityResolver, Deduplicator

Purpose: Build and manage knowledge graphs

from semantica.kg import GraphBuilder, EntityResolver, Deduplicator

# Build knowledge graph
graph_builder = GraphBuilder()
node = graph_builder.create_node("Apple Inc.", "Company")
edge = graph_builder.create_edge("Apple Inc.", "founded_by", "Steve Jobs")
subgraph = graph_builder.build_subgraph(entities)

# Entity resolution
resolver = EntityResolver()
canonical = resolver.resolve_identity("Apple Inc.", "Apple")
merged = resolver.merge_entities(duplicate_entities)

# Deduplication
deduplicator = Deduplicator()
duplicates = deduplicator.find_duplicates(entities)
merged = deduplicator.merge_duplicates(duplicates)

Submodules:

• graph_builder - Knowledge graph construction
• entity_resolver - Entity disambiguation and merging
• deduplicator - Duplicate detection and resolution
• seed_manager - Initial data loading
• provenance_tracker - Source tracking and confidence
• conflict_detector - Conflict identification and resolution

---

💾 Storage & Retrieval

10. Vector Store (semantica.vector_store)

Main Classes: PineconeAdapter, FAISSAdapter, WeaviateAdapter

Purpose: Store and search vector embeddings

from semantica.vector_store import PineconeAdapter, FAISSAdapter

# Pinecone integration
pinecone = PineconeAdapter()
pinecone.connect(api_key="your-key")
pinecone.create_index("semantica-index", dimension=1536)
pinecone.upsert_vectors(vectors, metadata)
results = pinecone.query_vectors(query_vector, top_k=10)

# FAISS integration
faiss = FAISSAdapter()
faiss.create_index("IVFFlat", dimension=1536)
faiss.add_vectors(vectors)
faiss.save_index("index.faiss")
similar = faiss.search_similar(query_vector, k=10)

Submodules:

• pinecone_adapter - Pinecone cloud vector database
• faiss_adapter - Facebook AI Similarity Search
• milvus_adapter - Milvus vector database
• weaviate_adapter - Weaviate vector database
• qdrant_adapter - Qdrant vector database
• hybrid_search - Vector + metadata search

11. Triple Store (semantica.triple_store)

Main Classes: BlazegraphAdapter, JenaAdapter, GraphDBAdapter

Purpose: Store and query RDF triples

from semantica.triple_store import BlazegraphAdapter, JenaAdapter

# Blazegraph integration
blazegraph = BlazegraphAdapter()
blazegraph.connect("http://localhost:9999/blazegraph")
blazegraph.bulk_load(triples)

# SPARQL queries
sparql_query = """
SELECT ?subject ?predicate ?object 
WHERE { ?subject ?predicate ?object }
LIMIT 10
"""
results = blazegraph.execute_sparql(sparql_query)

# Jena integration
jena = JenaAdapter()
model = jena.create_model()
jena.add_triples(model, triples)
inferred = jena.run_inference(model)

Submodules:

• blazegraph_adapter - Blazegraph SPARQL endpoint
• jena_adapter - Apache Jena RDF framework
• rdf4j_adapter - Eclipse RDF4J
• graphdb_adapter - GraphDB with reasoning
• virtuoso_adapter - Virtuoso RDF store

12. Embeddings (semantica.embeddings)

Main Class: SemanticEmbedder

Purpose: Generate semantic embeddings for text and multimodal content

from semantica.embeddings import SemanticEmbedder

# Initialize embedder
embedder = SemanticEmbedder(
    model="text-embedding-3-large",
    dimension=1536,
    preserve_context=True
)

# Generate embeddings
text_embeddings = embedder.embed_text("Hello world")
sentence_embeddings = embedder.embed_sentence("This is a sentence")
document_embeddings = embedder.embed_document(long_document)

# Batch processing
batch_embeddings = embedder.batch_process(texts)
stats = embedder.get_embedding_stats()

Submodules:

• text_embedder - Text-based embeddings
• image_embedder - Image embeddings and vision models
• audio_embedder - Audio embeddings and speech recognition
• multimodal_embedder - Cross-modal embeddings
• context_manager - Context window management
• pooling_strategies - Various pooling strategies

---

🤖 AI & Reasoning

13. RAG System (semantica.qa_rag)

Main Classes: RAGManager, SemanticChunker, AnswerBuilder

Purpose: Question answering and retrieval-augmented generation

from semantica.qa_rag import RAGManager, SemanticChunker

# Initialize RAG system
rag = RAGManager(
    retriever="semantic",
    generator="gpt-4",
    chunk_size=512,
    overlap=50
)

# Process question
question = "What are the key features of Semantica?"
answer = rag.process_question(question)
sources = rag.get_sources()
confidence = rag.get_confidence()

# Semantic chunking for RAG
chunker = SemanticChunker()
chunks = chunker.chunk_text(document, optimize_for_rag=True)

Submodules:

• semantic_chunker - RAG-optimized text chunking
• prompt_templates - RAG prompt templates
• retrieval_policies - Retrieval strategies and ranking
• answer_builder - Answer construction and attribution
• provenance_tracker - Source tracking and confidence
• conversation_manager - Multi-turn conversations

14. Reasoning Engine (semantica.reasoning)

Main Classes: InferenceEngine, SPARQLReasoner, AbductiveReasoner

Purpose: Logical reasoning and inference

from semantica.reasoning import InferenceEngine, SPARQLReasoner

# Rule-based inference
inference = InferenceEngine()
inference.add_rule("IF ?x is_a Company AND ?x founded_by ?y THEN ?y is_a Person")
inference.forward_chain()
inference.backward_chain()

# SPARQL reasoning
sparql_reasoner = SPARQLReasoner()
expanded_query = sparql_reasoner.expand_query(sparql_query)
inferred_results = sparql_reasoner.infer_results(query_results)

Submodules:

• inference_engine - Rule-based inference
• sparql_reasoner - SPARQL-based reasoning
• rete_engine - Rete algorithm implementation
• abductive_reasoner - Abductive reasoning
• deductive_reasoner - Deductive reasoning
• explanation_generator - Reasoning explanations

15. Multi-Agent System (semantica.agents)

Main Classes: AgentManager, OrchestrationEngine, MultiAgentManager

Purpose: Multi-agent coordination and workflows

from semantica.agents import AgentManager, OrchestrationEngine

# Agent management
agent_manager = AgentManager()
agent = agent_manager.register_agent("data_processor", capabilities=["parse", "extract"])
agent_manager.start_agent(agent)

# Multi-agent orchestration
orchestrator = OrchestrationEngine()
workflow = orchestrator.coordinate_agents([
    "ingestion_agent",
    "parsing_agent", 
    "extraction_agent",
    "embedding_agent"
])
results = orchestrator.distribute_tasks(workflow, tasks)

Submodules:

• agent_manager - Agent lifecycle management
• orchestration_engine - Multi-agent coordination
• tool_registry - Tool registration and discovery
• cost_tracker - Cost monitoring and optimization
• sandbox_manager - Agent sandboxing and security
• workflow_engine - Workflow definition and execution

---

🎯 Domain Specialization

16. Domain Processors (semantica.domains)

Main Classes: CybersecurityProcessor, BiomedicalProcessor, FinanceProcessor

Purpose: Domain-specific data processing and analysis

from semantica.domains import CybersecurityProcessor, FinanceProcessor

# Cybersecurity analysis
cyber = CybersecurityProcessor()
threats = cyber.detect_threats(security_logs)
attacks = cyber.analyze_attacks(incident_data)
risks = cyber.assess_risks(vulnerability_data)

# Financial analysis
finance = FinanceProcessor()
market_trends = finance.market_analysis(market_data)
risk_assessment = finance.risk_assessment(portfolio_data)
compliance = finance.compliance_checking(transaction_data)

Submodules:

• cybersecurity_processor - Security threat detection
• biomedical_processor - Medical data analysis
• finance_processor - Financial market analysis
• legal_processor - Legal document analysis
• domain_ontologies - Domain-specific ontologies
• domain_extractors - Specialized entity extractors

---

🖥️ User Interface

17. Web Dashboard (semantica.ui)

Main Classes: UIManager, KGViewer, AnalyticsDashboard

Purpose: Web-based user interface and visualization

from semantica.ui import UIManager, KGViewer, AnalyticsDashboard

# Initialize dashboard
ui_manager = UIManager()
dashboard = ui_manager.initialize_dashboard()

# Knowledge graph visualization
kg_viewer = KGViewer()
kg_viewer.display_graph(knowledge_graph)
kg_viewer.zoom_graph(zoom_level=1.5)
nodes = kg_viewer.search_nodes("Apple")

# Analytics dashboard
analytics = AnalyticsDashboard()
analytics.show_metrics(system_metrics)
charts = analytics.generate_charts(data)

Submodules:

• ingestion_monitor - Real-time ingestion monitoring
• kg_viewer - Interactive knowledge graph visualization
• conflict_resolver - Conflict resolution interface
• analytics_dashboard - Data analytics and visualization
• pipeline_editor - Visual pipeline builder
• data_explorer - Data exploration interface

---

⚙️ Operations

18. Streaming (semantica.streaming)

Main Classes: KafkaAdapter, StreamProcessor, CheckpointManager

Purpose: Real-time data streaming and processing

from semantica.streaming import KafkaAdapter, StreamProcessor

# Kafka streaming
kafka = KafkaAdapter()
kafka.connect("localhost:9092")
kafka.create_topic("semantica-events")
kafka.produce_message("semantica-events", message)

# Stream processing
processor = StreamProcessor()
processor.process_stream(kafka_stream)
processor.apply_windowing(window_size="5m")
aggregated = processor.aggregate_data(stream_data)

Submodules:

• kafka_adapter - Apache Kafka integration
• pulsar_adapter - Apache Pulsar integration
• rabbitmq_adapter - RabbitMQ integration
• kinesis_adapter - AWS Kinesis integration
• stream_processor - Stream processing logic
• checkpoint_manager - Checkpoint and recovery

19. Monitoring (semantica.monitoring)

Main Classes: MetricsCollector, HealthChecker, AlertManager

Purpose: System monitoring and observability

from semantica.monitoring import MetricsCollector, HealthChecker, AlertManager

# Metrics collection
metrics = MetricsCollector()
metrics.collect_metrics()
performance = metrics.monitor_performance()
resources = metrics.track_resources()

# Health checking
health = HealthChecker()
system_health = health.check_system_health()
component_status = health.check_component_status()

# Alert management
alerts = AlertManager()
alerts.generate_alert("High CPU usage detected")
alerts.route_notification("admin@example.com")

Submodules:

• metrics_collector - System metrics collection
• tracing_system - OpenTelemetry distributed tracing
• alert_manager - Alert generation and routing
• sla_monitor - SLA tracking and compliance
• quality_metrics - Data quality assessment
• log_manager - Log collection and analysis

20. Quality Assurance (semantica.quality)

Main Classes: QAEngine, ValidationEngine, TripleValidator

Purpose: Data quality validation and testing

from semantica.quality import QAEngine, ValidationEngine, TripleValidator

# Quality assurance
qa = QAEngine()
qa_tests = qa.run_qa_tests(data)
validation = qa.validate_data(data)
report = qa.generate_reports()

# Data validation
validator = ValidationEngine()
validated = validator.validate_data(data, schema)
constraints = validator.check_constraints(data)

# Triple validation
triple_validator = TripleValidator()
valid_triples = triple_validator.validate_triple(triple)
consistency = triple_validator.check_consistency(triples)
quality_score = triple_validator.score_quality(triples)

Submodules:

• qa_engine - Quality assurance testing
• validation_engine - Data validation and schema checking
• triple_validator - RDF triple validation
• confidence_calculator - Confidence scoring
• test_generator - Automated test generation
• compliance_checker - Regulatory compliance checking

21. Security (semantica.security)

Main Classes: AccessControl, DataMasking, PIIRedactor

Purpose: Security and privacy protection

from semantica.security import AccessControl, DataMasking, PIIRedactor

# Access control
access = AccessControl()
access.authenticate_user(username, password)
access.authorize_access(user, resource)
access.manage_roles(user, roles)

# Data masking
masking = DataMasking()
masked_data = masking.mask_sensitive_data(data)
anonymized = masking.anonymize_data(personal_data)

# PII redaction
pii_redactor = PIIRedactor()
pii_detected = pii_redactor.detect_pii(text)
redacted = pii_redactor.redact_pii(text)

Submodules:

• access_control - Role-based access control
• data_masking - Sensitive data masking
• pii_redactor - PII detection and redaction
• audit_logger - Audit trail logging
• encryption_manager - Data encryption and key management
• threat_monitor - Threat monitoring and detection

22. CLI Tools (semantica.cli)

Main Classes: IngestionCLI, KBBuilderCLI, ExportCLI

Purpose: Command-line interface tools

from semantica.cli import IngestionCLI, KBBuilderCLI, ExportCLI

# Command line usage
# semantica ingest --source documents/ --format pdf,docx
# semantica build-kb --config config.yaml
# semantica export --format turtle --output knowledge.ttl

# Programmatic CLI
ingestion_cli = IngestionCLI()
ingestion_cli.ingest_files(["doc1.pdf", "doc2.docx"])
ingestion_cli.track_progress()

kb_builder = KBBuilderCLI()
kb_builder.build_kb(config_file="config.yaml")
kb_builder.monitor_build()

export_cli = ExportCLI()
export_cli.export_triples(format="turtle", output="output.ttl")

Submodules:

• ingestion_cli - File ingestion commands
• kb_builder_cli - Knowledge base building
• export_cli - Data export utilities
• qa_cli - Quality assurance tools
• monitoring_cli - System monitoring commands
• interactive_shell - Interactive command shell

---

🚀 Quick Start Examples

Complete Pipeline Example

from semantica import Semantica
from semantica.pipeline import PipelineBuilder

# Initialize Semantica
core = Semantica(
    llm_provider="openai",
    embedding_model="text-embedding-3-large",
    vector_store="pinecone",
    graph_db="neo4j"
)

# Build processing pipeline
pipeline = PipelineBuilder() \
    .add_step("ingest", {"source": "documents/", "formats": ["pdf", "docx"]}) \
    .add_step("parse", {"extract_tables": True, "extract_images": True}) \
    .add_step("normalize", {"clean_text": True, "detect_language": True}) \
    .add_step("chunk", {"strategy": "semantic", "size": 512}) \
    .add_step("extract", {"entities": True, "relations": True, "triples": True}) \
    .add_step("embed", {"model": "text-embedding-3-large"}) \
    .add_step("store", {"vector_store": "pinecone", "triple_store": "neo4j"}) \
    .set_parallelism(4) \
    .build()

# Execute pipeline
results = pipeline.run()

# Query results
knowledge_base = core.build_knowledge_base("documents/")
answer = knowledge_base.query("What are the main topics?")

Domain-Specific Example

from semantica.domains import FinanceProcessor
from semantica.qa_rag import RAGManager

# Financial analysis
finance = FinanceProcessor()
market_data = finance.market_analysis("market_data.csv")
risk_assessment = finance.risk_assessment("portfolio.json")

# RAG for financial Q&A
rag = RAGManager(
    retriever="semantic",
    generator="gpt-4",
    domain="finance"
)

# Process financial questions
question = "What are the risk factors for this portfolio?"
answer = rag.process_question(question, context=market_data)

---

📚 Additional Resources

• Documentation: https://semantica.readthedocs.io/
• API Reference: https://semantica.readthedocs.io/api/
• Examples Repository: https://github.com/semantica/examples
• Community: https://discord.gg/semantica

---

📚 Complete Module Index

All 22 Main Modules with Submodules

#	Module	Package	Main Classes	Submodules Count
1	Core Engine	semantica.core	Semantica, Config, PluginManager	4
2	Pipeline Builder	semantica.pipeline	PipelineBuilder, ExecutionEngine	7
3	Data Ingestion	semantica.ingest	FileIngestor, WebIngestor, FeedIngestor	7
4	Document Parsing	semantica.parse	PDFParser, DOCXParser, HTMLParser	9
5	Text Normalization	semantica.normalize	TextCleaner, LanguageDetector	6
6	Text Chunking	semantica.split	SemanticChunker, StructuralChunker	5
7	Semantic Extraction	semantica.semantic_extract	NERExtractor, RelationExtractor	6
8	Ontology Generation	semantica.ontology	OntologyGenerator, ClassInferrer	6
9	Knowledge Graph	semantica.kg	GraphBuilder, EntityResolver	7
10	Vector Store	semantica.vector_store	PineconeAdapter, FAISSAdapter	6
11	Triple Store	semantica.triple_store	BlazegraphAdapter, JenaAdapter	5
12	Embeddings	semantica.embeddings	SemanticEmbedder, TextEmbedder	6
13	RAG System	semantica.qa_rag	RAGManager, SemanticChunker	7
14	Reasoning Engine	semantica.reasoning	InferenceEngine, SPARQLReasoner	7
15	Multi-Agent System	semantica.agents	AgentManager, OrchestrationEngine	8
16	Domain Processors	semantica.domains	CybersecurityProcessor, FinanceProcessor	6
17	Web Dashboard	semantica.ui	UIManager, KGViewer	8
18	Streaming	semantica.streaming	KafkaAdapter, StreamProcessor	6
19	Monitoring	semantica.monitoring	MetricsCollector, HealthChecker	7
20	Quality Assurance	semantica.quality	QAEngine, ValidationEngine	7
21	Security	semantica.security	AccessControl, DataMasking	7
22	CLI Tools	semantica.cli	IngestionCLI, KBBuilderCLI	6

Total: 22 Main Modules, 140+ Submodules

---

📥 Import Reference

Complete Import Guide

# =============================================================================
# CORE MODULES
# =============================================================================

# Main Semantica class
from semantica import Semantica
from semantica.core import Config, PluginManager, Orchestrator, LifecycleManager

# Pipeline management
from semantica.pipeline import (
    PipelineBuilder, ExecutionEngine, FailureHandler, 
    ParallelismManager, ResourceScheduler, PipelineValidator,
    MonitoringHooks, PipelineTemplates, PipelineManager
)

# =============================================================================
# DATA PROCESSING MODULES
# =============================================================================

# Data ingestion
from semantica.ingest import (
    FileIngestor, WebIngestor, FeedIngestor, StreamIngestor,
    RepoIngestor, EmailIngestor, DBIngestor, IngestManager,
    ConnectorRegistry
)

# Document parsing
from semantica.parse import (
    PDFParser, DOCXParser, PPTXParser, ExcelParser, HTMLParser,
    JSONLParser, CSVParser, LaTeXParser, ImageParser, TableParser,
    ParserRegistry
)

# Text normalization
from semantica.normalize import (
    TextCleaner, LanguageDetector, EncodingHandler, EntityNormalizer,
    DateNormalizer, NumberNormalizer, NormalizationPipeline
)

# Text chunking
from semantica.split import (
    SlidingWindowChunker, SemanticChunker, StructuralChunker,
    TableChunker, ProvenanceTracker, ChunkValidator, SplitManager
)

# =============================================================================
# SEMANTIC INTELLIGENCE MODULES
# =============================================================================

# Semantic extraction
from semantica.semantic_extract import (
    NERExtractor, RelationExtractor, EventDetector, CorefResolver,
    TripleExtractor, LLMEnhancer, ExtractionValidator, ExtractionPipeline
)

# Ontology generation
from semantica.ontology import (
    OntologyGenerator, ClassInferrer, PropertyGenerator, OWLGenerator,
    BaseMapper, VersionManager, OntologyValidator, DomainOntologies,
    OntologyManager
)

# Knowledge graph
from semantica.kg import (
    GraphBuilder, EntityResolver, Deduplicator, SeedManager,
    ProvenanceTracker, ConflictDetector, GraphValidator, GraphAnalyzer,
    KnowledgeGraphManager
)

# =============================================================================
# STORAGE & RETRIEVAL MODULES
# =============================================================================

# Vector stores
from semantica.vector_store import (
    PineconeAdapter, FAISSAdapter, MilvusAdapter, WeaviateAdapter,
    QdrantAdapter, NamespaceManager, MetadataStore, HybridSearch,
    IndexOptimizer, VectorStoreManager
)

# Triple stores
from semantica.triple_store import (
    BlazegraphAdapter, JenaAdapter, RDF4JAdapter, GraphDBAdapter,
    VirtuosoAdapter, TripleManager, QueryEngine, BulkLoader,
    TripleStoreManager
)

# Embeddings
from semantica.embeddings import (
    SemanticEmbedder, TextEmbedder, ImageEmbedder, AudioEmbedder,
    MultimodalEmbedder, ContextManager, PoolingStrategies,
    ProviderAdapter, EmbeddingOptimizer
)

# =============================================================================
# AI & REASONING MODULES
# =============================================================================

# RAG system
from semantica.qa_rag import (
    RAGManager, SemanticChunker, PromptTemplates, RetrievalPolicies,
    AnswerBuilder, ProvenanceTracker, AnswerValidator, RAGOptimizer,
    ConversationManager
)

# Reasoning engine
from semantica.reasoning import (
    InferenceEngine, SPARQLReasoner, ReteEngine, AbductiveReasoner,
    DeductiveReasoner, RuleManager, ReasoningValidator, ExplanationGenerator,
    ReasoningManager
)

# Multi-agent system
from semantica.agents import (
    AgentManager, OrchestrationEngine, ToolRegistry, CostTracker,
    SandboxManager, WorkflowEngine, AgentCommunication, PolicyEnforcer,
    AgentAnalytics, MultiAgentManager
)

# =============================================================================
# DOMAIN SPECIALIZATION MODULES
# =============================================================================

# Domain processors
from semantica.domains import (
    CybersecurityProcessor, BiomedicalProcessor, FinanceProcessor,
    LegalProcessor, DomainTemplates, MappingRules, DomainOntologies,
    DomainExtractors, DomainValidator, DomainManager
)

# =============================================================================
# USER INTERFACE MODULES
# =============================================================================

# Web dashboard
from semantica.ui import (
    UIManager, IngestionMonitor, KGViewer, ConflictResolver,
    AnalyticsDashboard, PipelineEditor, DataExplorer, UserManagement,
    NotificationSystem, ReportGenerator
)

# =============================================================================
# OPERATIONS MODULES
# =============================================================================

# Streaming
from semantica.streaming import (
    KafkaAdapter, PulsarAdapter, RabbitMQAdapter, KinesisAdapter,
    StreamProcessor, CheckpointManager, ExactlyOnce, StreamMonitor,
    BackpressureHandler, StreamingManager
)

# Monitoring
from semantica.monitoring import (
    MetricsCollector, TracingSystem, AlertManager, SLAMonitor,
    QualityMetrics, HealthChecker, PerformanceAnalyzer, LogManager,
    DashboardRenderer, MonitoringManager
)

# Quality assurance
from semantica.quality import (
    QAEngine, ValidationEngine, SchemaValidator, TripleValidator,
    ConfidenceCalculator, TestGenerator, QualityReporter, DataProfiler,
    ComplianceChecker, QualityManager
)

# Security
from semantica.security import (
    AccessControl, DataMasking, PIIRedactor, AuditLogger,
    EncryptionManager, SecurityValidator, ComplianceManager,
    ThreatMonitor, VulnerabilityScanner, SecurityManager
)

# CLI tools
from semantica.cli import (
    IngestionCLI, KBBuilderCLI, ExportCLI, QACLI, MonitoringCLI,
    PipelineCLI, UserManagementCLI, HelpSystem, InteractiveShell
)

# =============================================================================
# UTILITY MODULES
# =============================================================================

# Additional utilities
from semantica.utils import (
    DataValidator, SchemaManager, TemplateManager, SeedManager,
    SemanticDeduplicator, ConflictDetector, SecurityConfig,
    MultiProviderConfig, AnalyticsDashboard, BusinessIntelligenceDashboard,
    HealthcareProcessor, CyberSecurityProcessor, EnterpriseDeployment
)

# =============================================================================
# QUICK IMPORTS FOR COMMON USE CASES
# =============================================================================

# Basic usage
from semantica import Semantica
from semantica.processors import DocumentProcessor, WebProcessor, FeedProcessor
from semantica.context import ContextEngineer
from semantica.embeddings import SemanticEmbedder
from semantica.graph import KnowledgeGraphBuilder
from semantica.query import SPARQLQueryGenerator
from semantica.streaming import StreamProcessor, LiveFeedMonitor
from semantica.pipelines import ResearchPipeline, BusinessIntelligenceDashboard
from semantica.healthcare import HealthcareProcessor
from semantica.security import CyberSecurityProcessor
from semantica.deployment import EnterpriseDeployment
from semantica.analytics import AnalyticsDashboard
from semantica.quality import QualityAssurance

# Advanced usage
from semantica.config import MultiProviderConfig
from semantica.security import SecurityConfig

---

🔧 Module Dependencies

Core Dependencies

# Required for all modules
semantica[core] >= 1.0.0

# Optional dependencies by module
semantica[pdf]          # PDF parsing
semantica[web]          # Web scraping
semantica[feeds]        # RSS/Atom feeds
semantica[office]       # Office documents
semantica[scientific]   # Scientific formats
semantica[all]          # All dependencies

External Dependencies

# Vector stores
pinecone-client >= 2.0.0
faiss-cpu >= 1.7.0
weaviate-client >= 3.0.0

# Triple stores
rdflib >= 6.0.0
sparqlwrapper >= 2.0.0

# ML/AI
openai >= 1.0.0
transformers >= 4.20.0
torch >= 1.12.0

# Data processing
pandas >= 1.5.0
numpy >= 1.21.0
spacy >= 3.4.0

---

📊 Module Statistics

• Total Main Modules: 22
• Total Submodules: 140+
• Total Classes: 200+
• Total Functions: 1000+
• Supported Formats: 50+
• Supported Languages: 100+
• Integration Points: 30+

---

This comprehensive module reference covers all major components of the Semantica toolkit. Each module is designed to be modular, extensible, and production-ready for enterprise use cases.