semantica/Modules.md

# 🧩 Semantica Modules & Submodules

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

---

## 📋 Table of Contents

1. [Core Modules](#core-modules)
2. [Data Processing](#data-processing)
3. [Semantic Intelligence](#semantic-intelligence)
4. [Storage & Retrieval](#storage--retrieval)
5. [AI & Reasoning](#ai--reasoning)
6. [Domain Specialization](#domain-specialization)
7. [User Interface](#user-interface)
8. [Operations](#operations)
9. [Complete Module Index](#complete-module-index)
10. [Import Reference](#import-reference)

---

## 🏗️ Core Modules

### 1. **Core Engine** (`semantica.core`)

**Main Class:** `Semantica`

**Purpose:** Central orchestration, configuration, and pipeline management

#### **Imports:**
```python
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:**
```python
# 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`):**
```python
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`):**
```python
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`):**
```python
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`):**
```python
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:**
```python
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:**
```python
# 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`):**
```python
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`):**
```python
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`):**
```python
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`):**
```python
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`):**
```python
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`):**
```python
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`):**
```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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/](https://semantica.readthedocs.io/)
- **API Reference**: [https://semantica.readthedocs.io/api/](https://semantica.readthedocs.io/api/)
- **Examples Repository**: [https://github.com/semantica/examples](https://github.com/semantica/examples)
- **Community**: [https://discord.gg/semantica](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**

---

## 🔧 Complete Functions Reference

### Module-by-Module Function Tables

#### 1. **Core Engine Functions** (`semantica.core`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `Semantica.initialize()` | core | Setup all modules and connections | None | Status |
| `Semantica.build_knowledge_base()` | core | Process data sources into knowledge base | sources: List[str] | KnowledgeBase |
| `Semantica.get_status()` | core | Get system health and metrics | None | Dict |
| `Semantica.create_pipeline()` | core | Create processing pipeline | config: Dict | Pipeline |
| `Semantica.get_config()` | core | Get current configuration | None | Config |
| `Semantica.list_plugins()` | core | List available plugins | None | List[Plugin] |
| `Config.validate()` | config_manager | Validate configuration against schema | schema: str | bool |
| `PluginManager.load_plugin()` | plugin_registry | Dynamically load plugin modules | name: str, version: str | Plugin |
| `PluginManager.list_plugins()` | plugin_registry | Show available plugins and versions | None | List[Plugin] |
| `Orchestrator.schedule_pipeline()` | orchestrator | Schedule pipeline execution | pipeline_config: Dict | PipelineID |
| `Orchestrator.monitor_progress()` | orchestrator | Monitor pipeline progress | pipeline_id: str | Progress |
| `LifecycleManager.startup()` | lifecycle | Execute startup hooks | None | Status |
| `LifecycleManager.shutdown()` | lifecycle | Execute shutdown hooks | None | Status |

#### 2. **Pipeline Builder Functions** (`semantica.pipeline`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `PipelineBuilder.add_step()` | pipeline | Add processing step to pipeline | name: str, config: Dict | PipelineBuilder |
| `PipelineBuilder.set_parallelism()` | pipeline | Configure parallel execution | level: int | PipelineBuilder |
| `PipelineBuilder.build()` | pipeline | Build the pipeline | None | Pipeline |
| `Pipeline.run()` | execution_engine | Execute complete pipeline | None | Results |
| `Pipeline.pause()` | execution_engine | Pause pipeline execution | None | Status |
| `Pipeline.resume()` | execution_engine | Resume paused pipeline | None | Status |
| `Pipeline.stop()` | execution_engine | Stop pipeline execution | None | Status |
| `ExecutionEngine.execute_pipeline()` | execution_engine | Execute pipeline with config | config: Dict | Results |
| `FailureHandler.retry_step()` | failure_handler | Retry failed step | step_id: str, error: Exception | Status |
| `FailureHandler.handle_error()` | failure_handler | Handle execution errors | error: Exception | RecoveryPlan |
| `ParallelExecutor.execute_parallel()` | parallelism_manager | Execute tasks in parallel | tasks: List[Task] | Results |
| `ResourceScheduler.allocate_cpu()` | resource_scheduler | Allocate CPU resources | cores: int | ResourceID |
| `ResourceScheduler.allocate_gpu()` | resource_scheduler | Allocate GPU resources | device_id: int | ResourceID |
| `PipelineValidator.validate_pipeline()` | pipeline_validator | Validate pipeline configuration | config: Dict | ValidationResult |

#### 3. **Data Ingestion Functions** (`semantica.ingest`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `FileIngestor.scan_directory()` | file | Recursively scan directory for files | path: str, recursive: bool | List[File] |
| `FileIngestor.detect_format()` | file | Auto-detect file type and encoding | file_path: str | FileFormat |
| `WebIngestor.crawl_site()` | web | Crawl website with depth and rate limiting | url: str, max_depth: int | WebContent |
| `WebIngestor.extract_links()` | web | Extract and follow hyperlinks | content: WebContent | List[Link] |
| `FeedIngestor.parse_rss()` | feed | Parse RSS/Atom feeds with metadata | feed_url: str | FeedData |
| `StreamIngestor.connect()` | stream | Establish real-time data connection | config: Dict | StreamConnection |
| `RepoIngestor.clone_repo()` | repo | Clone and track repository changes | repo_url: str | Repository |
| `EmailIngestor.connect_imap()` | email | Connect to email server | server: str, credentials: Dict | EmailConnection |
| `DBIngestor.export_table()` | db_export | Export database table to structured format | table: str, query: str | StructuredData |
| `IngestManager.resume_from_token()` | ingest | Resume interrupted ingestion | token: str | Status |
| `IngestManager.get_progress()` | ingest | Monitor ingestion progress | None | Progress |
| `ConnectorRegistry.register()` | ingest | Register custom data connectors | connector: Connector | Status |

#### 4. **Document Parsing Functions** (`semantica.parse`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `PDFParser.extract_text()` | pdf | Extract text with positioning and formatting | file_path: str | TextContent |
| `PDFParser.extract_tables()` | pdf | Extract tables using Camelot/Tabula | file_path: str | List[Table] |
| `PDFParser.extract_images()` | pdf | Extract embedded images and figures | file_path: str | List[Image] |
| `DOCXParser.get_document_structure()` | docx | Extract document outline and sections | file_path: str | DocumentStructure |
| `DOCXParser.extract_track_changes()` | docx | Extract revision history | file_path: str | TrackChanges |
| `PPTXParser.extract_slides()` | pptx | Extract slide content and speaker notes | file_path: str | List[Slide] |
| `ExcelParser.read_sheet()` | excel | Read specific worksheet with data types | file_path: str, sheet: str | Worksheet |
| `ExcelParser.extract_charts()` | excel | Extract chart data and metadata | file_path: str | List[Chart] |
| `HTMLParser.parse_dom()` | html | Parse HTML into structured DOM tree | url: str | DOMTree |
| `HTMLParser.extract_metadata()` | html | Extract meta tags and structured data | dom: DOMTree | Metadata |
| `ImageParser.ocr_text()` | images | Perform OCR using Tesseract/Google Vision | image_path: str | TextContent |
| `ImageParser.detect_objects()` | images | Detect objects and faces in images | image_path: str | List[Object] |
| `TableParser.detect_structure()` | tables | Detect table boundaries and headers | content: str | TableStructure |
| `TableParser.extract_cells()` | tables | Extract individual cell data | table: Table | List[Cell] |
| `ParserRegistry.get_parser()` | parse | Get appropriate parser for file type | file_type: str | Parser |
| `ParserRegistry.supported_formats()` | parse | List all supported file formats | None | List[str] |

#### 5. **Text Normalization Functions** (`semantica.normalize`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `TextCleaner.remove_html()` | text_cleaner | Strip HTML tags and preserve text content | html: str | str |
| `TextCleaner.normalize_whitespace()` | text_cleaner | Standardize spacing and line breaks | text: str | str |
| `TextCleaner.remove_special_chars()` | text_cleaner | Clean special characters and symbols | text: str | str |
| `LanguageDetector.detect()` | language_detector | Identify text language with confidence score | text: str | Language |
| `LanguageDetector.supported_languages()` | language_detector | List all supported languages | None | List[str] |
| `EncodingHandler.normalize()` | encoding_handler | Convert to UTF-8 and validate encoding | text: bytes | str |
| `EncodingHandler.detect_encoding()` | encoding_handler | Auto-detect file encoding | file_path: str | str |
| `EntityNormalizer.canonicalize()` | entity_normalizer | Standardize entity names and aliases | entity: str, alias: str | str |
| `EntityNormalizer.expand_acronyms()` | entity_normalizer | Expand abbreviations and acronyms | text: str | str |
| `DateNormalizer.parse_date()` | date_normalizer | Parse various date formats to ISO standard | date_str: str | datetime |
| `DateNormalizer.resolve_relative()` | date_normalizer | Convert relative dates to absolute | date_str: str | datetime |
| `NumberNormalizer.standardize()` | number_normalizer | Convert numbers to standard format | number: str | str |
| `NumberNormalizer.convert_units()` | number_normalizer | Convert between measurement units | value: float, from_unit: str, to_unit: str | float |
| `NormalizationPipeline.run()` | normalize | Execute complete normalization pipeline | text: str | NormalizedText |
| `NormalizationPipeline.get_stats()` | normalize | Return normalization statistics | None | Dict |

#### 6. **Text Chunking Functions** (`semantica.split`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `SlidingWindowChunker.split()` | sliding_window | Create fixed-size chunks with overlap | text: str, size: int, overlap: int | List[Chunk] |
| `SlidingWindowChunker.set_window_size()` | sliding_window | Configure chunk size and overlap | size: int, overlap: int | None |
| `SemanticChunker.split_by_meaning()` | semantic_chunker | Split based on semantic boundaries | text: str | List[Chunk] |
| `SemanticChunker.detect_topics()` | semantic_chunker | Identify topic changes for splitting | text: str | List[Topic] |
| `StructuralChunker.split_by_sections()` | structural_chunker | Split on document structure | document: Document | List[Chunk] |
| `StructuralChunker.identify_headers()` | structural_chunker | Detect section headers and levels | document: Document | List[Header] |
| `TableChunker.preserve_tables()` | table_chunker | Keep tables intact during splitting | document: Document | List[Chunk] |
| `TableChunker.extract_table_context()` | table_chunker | Extract surrounding context for tables | table: Table | str |
| `ProvenanceTracker.track_source()` | provenance_tracker | Track original source and position | chunk: Chunk | Provenance |
| `ProvenanceTracker.get_provenance()` | provenance_tracker | Retrieve chunk source information | chunk_id: str | Provenance |
| `ChunkValidator.validate_chunk()` | chunk_validator | Validate chunk quality and size | chunk: Chunk | ValidationResult |
| `ChunkValidator.detect_overlaps()` | chunk_validator | Find overlapping chunks | chunks: List[Chunk] | List[Overlap] |
| `SplitManager.run_strategy()` | split | Execute chosen splitting strategy | text: str, strategy: str | List[Chunk] |
| `SplitManager.get_chunk_stats()` | split | Return chunking statistics | None | Dict |

#### 7. **Semantic Extraction Functions** (`semantica.semantic_extract`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `NERExtractor.extract_entities()` | ner_extractor | Extract named entities with types and confidence | text: str | List[Entity] |
| `NERExtractor.classify_entities()` | ner_extractor | Classify entities into predefined categories | entities: List[Entity] | List[ClassifiedEntity] |
| `RelationExtractor.find_relations()` | relation_extractor | Detect relationships between entities | text: str | List[Relation] |
| `RelationExtractor.classify_relations()` | relation_extractor | Classify relation types and directions | relations: List[Relation] | List[ClassifiedRelation] |
| `EventDetector.detect_events()` | event_detector | Identify events and their participants | text: str | List[Event] |
| `EventDetector.extract_temporal()` | event_detector | Extract temporal information for events | events: List[Event] | List[TemporalInfo] |
| `CorefResolver.resolve_references()` | coref_resolver | Resolve co-references and pronouns | text: str | List[Resolution] |
| `CorefResolver.link_entities()` | coref_resolver | Link entities across document sections | entities: List[Entity] | List[Link] |
| `TripleExtractor.extract_triples()` | triple_extractor | Extract RDF-style triples | text: str | List[Triple] |
| `TripleExtractor.validate_triples()` | triple_extractor | Validate triple structure and consistency | triples: List[Triple] | List[ValidatedTriple] |
| `LLMEnhancer.enhance_extraction()` | llm_enhancer | Use LLM for complex extraction tasks | text: str, task: str | EnhancedResults |
| `LLMEnhancer.detect_patterns()` | llm_enhancer | Identify complex patterns and relationships | text: str | List[Pattern] |
| `ExtractionValidator.validate_quality()` | extraction_validator | Assess extraction quality | results: ExtractionResults | QualityScore |
| `ExtractionValidator.filter_by_confidence()` | extraction_validator | Filter results by confidence score | results: List[Result], threshold: float | List[Result] |
| `ExtractionPipeline.run()` | semantic_extract | Execute complete extraction pipeline | text: str | ExtractionResults |

#### 8. **Ontology Generation Functions** (`semantica.ontology`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `ClassInferrer.infer_classes()` | class_inferrer | Automatically discover entity classes | entities: List[Entity] | List[Class] |
| `ClassInferrer.build_hierarchy()` | class_inferrer | Build class inheritance hierarchy | classes: List[Class] | Hierarchy |
| `ClassInferrer.analyze_relationships()` | class_inferrer | Analyze class relationships and dependencies | classes: List[Class] | List[Relationship] |
| `PropertyGenerator.infer_properties()` | property_generator | Infer object and data properties | classes: List[Class] | List[Property] |
| `PropertyGenerator.detect_data_types()` | property_generator | Detect property data types and constraints | properties: List[Property] | List[DataType] |
| `PropertyGenerator.analyze_cardinality()` | property_generator | Analyze property cardinality | properties: List[Property] | List[Cardinality] |
| `OWLGenerator.generate_owl()` | owl_generator | Generate OWL ontology in RDF/XML format | ontology: Ontology | str |
| `OWLGenerator.serialize_rdf()` | owl_generator | Serialize to various RDF formats | ontology: Ontology, format: str | str |
| `BaseMapper.map_to_schema_org()` | base_mapper | Map entities to schema.org vocabulary | entities: List[Entity] | List[Mapping] |
| `BaseMapper.map_to_foaf()` | base_mapper | Map to FOAF ontology | entities: List[Entity] | List[Mapping] |
| `BaseMapper.map_to_dublin_core()` | base_mapper | Map to Dublin Core metadata standards | entities: List[Entity] | List[Mapping] |
| `VersionManager.create_version()` | version_manager | Create new ontology version | ontology: Ontology | Version |
| `VersionManager.track_changes()` | version_manager | Track changes between versions | old_version: Version, new_version: Version | List[Change] |
| `VersionManager.migrate_ontology()` | version_manager | Support ontology migration and updates | old_ontology: Ontology, new_schema: Schema | Ontology |
| `OntologyValidator.validate_schema()` | ontology_validator | Validate ontology schema consistency | ontology: Ontology | ValidationResult |
| `OntologyValidator.check_constraints()` | ontology_validator | Check ontology constraint violations | ontology: Ontology | List[Violation] |
| `DomainOntologies.get_finance_ontology()` | domain_ontologies | Get pre-built financial ontology | None | Ontology |
| `DomainOntologies.get_healthcare_ontology()` | domain_ontologies | Get pre-built healthcare ontology | None | Ontology |
| `OntologyManager.build_ontology()` | ontology | Build complete ontology from extracted data | data: ExtractedData | Ontology |
| `OntologyManager.export_ontology()` | ontology | Export ontology in various formats | ontology: Ontology, format: str | str |

#### 9. **Knowledge Graph Functions** (`semantica.kg`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `GraphBuilder.create_node()` | graph_builder | Create knowledge graph node | id: str, type: str, properties: Dict | Node |
| `GraphBuilder.create_edge()` | graph_builder | Create relationship edge between nodes | from_node: str, to_node: str, relation: str | Edge |
| `GraphBuilder.build_subgraph()` | graph_builder | Build subgraph from specific entities | entities: List[Entity] | SubGraph |
| `GraphBuilder.merge_graphs()` | graph_builder | Merge multiple knowledge graphs | graphs: List[Graph] | Graph |
| `EntityResolver.resolve_identity()` | entity_resolver | Resolve entity identity across sources | entity1: Entity, entity2: Entity | Resolution |
| `EntityResolver.merge_entities()` | entity_resolver | Merge duplicate entities | entities: List[Entity] | MergedEntity |
| `EntityResolver.get_canonical()` | entity_resolver | Get canonical entity representation | entity: Entity | Entity |
| `Deduplicator.find_duplicates()` | deduplicator | Find duplicate entities | entities: List[Entity] | List[Duplicate] |
| `Deduplicator.merge_duplicates()` | deduplicator | Merge duplicate entities | duplicates: List[Duplicate] | List[MergedEntity] |
| `Deduplicator.validate_merge()` | deduplicator | Validate merge operation | merge: MergeOperation | ValidationResult |
| `SeedManager.load_seed_data()` | seed_manager | Load initial seed data | data_source: str | SeedData |
| `SeedManager.validate_seed_data()` | seed_manager | Validate seed data quality | seed_data: SeedData | ValidationResult |
| `SeedManager.update_seed_data()` | seed_manager | Update existing seed data | seed_data: SeedData | Status |
| `ProvenanceTracker.track_source()` | provenance_tracker | Track information source | information: Information | Provenance |
| `ProvenanceTracker.get_provenance()` | provenance_tracker | Retrieve provenance information | info_id: str | Provenance |
| `ProvenanceTracker.calculate_confidence()` | provenance_tracker | Calculate confidence scores | provenance: Provenance | float |
| `ConflictDetector.detect_conflicts()` | conflict_detector | Detect conflicts between sources | sources: List[Source] | List[Conflict] |
| `ConflictDetector.classify_severity()` | conflict_detector | Classify conflict severity | conflict: Conflict | Severity |
| `ConflictDetector.create_resolution_workflow()` | conflict_detector | Create resolution workflow | conflicts: List[Conflict] | Workflow |
| `GraphValidator.validate_consistency()` | graph_validator | Validate graph consistency | graph: Graph | ValidationResult |
| `GraphValidator.check_schema_compliance()` | graph_validator | Check schema compliance | graph: Graph, schema: Schema | ComplianceResult |
| `GraphValidator.calculate_quality_metrics()` | graph_validator | Calculate quality metrics | graph: Graph | QualityMetrics |
| `GraphAnalyzer.calculate_centrality()` | graph_analyzer | Calculate node centrality | graph: Graph | CentralityScores |
| `GraphAnalyzer.detect_communities()` | graph_analyzer | Detect community structures | graph: Graph | List[Community] |
| `GraphAnalyzer.analyze_connectivity()` | graph_analyzer | Analyze graph connectivity | graph: Graph | ConnectivityMetrics |
| `KnowledgeGraphManager.build_graph()` | kg | Build complete knowledge graph | data: ProcessedData | KnowledgeGraph |
| `KnowledgeGraphManager.export_graph()` | kg | Export graph in various formats | graph: Graph, format: str | str |
| `KnowledgeGraphManager.visualize_graph()` | kg | Generate graph visualizations | graph: Graph | Visualization |

#### 10. **Vector Store Functions** (`semantica.vector_store`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `PineconeAdapter.connect()` | pinecone_adapter | Connect to Pinecone service | api_key: str | Connection |
| `PineconeAdapter.create_index()` | pinecone_adapter | Create new vector index | name: str, dimension: int | Index |
| `PineconeAdapter.upsert_vectors()` | pinecone_adapter | Insert or update vectors | vectors: List[Vector], metadata: Dict | Status |
| `PineconeAdapter.query_vectors()` | pinecone_adapter | Query similar vectors | query_vector: Vector, top_k: int | List[Result] |
| `FAISSAdapter.create_index()` | faiss_adapter | Create FAISS index | index_type: str, dimension: int | Index |
| `FAISSAdapter.add_vectors()` | faiss_adapter | Add vectors to index | vectors: List[Vector] | Status |
| `FAISSAdapter.search_similar()` | faiss_adapter | Search for similar vectors | query_vector: Vector, k: int | List[Result] |
| `FAISSAdapter.save_index()` | faiss_adapter | Save index to disk | file_path: str | Status |
| `MilvusAdapter.create_collection()` | milvus_adapter | Create Milvus collection | name: str, schema: Schema | Collection |
| `MilvusAdapter.insert_vectors()` | milvus_adapter | Insert vectors into collection | vectors: List[Vector] | Status |
| `MilvusAdapter.search_vectors()` | milvus_adapter | Search vectors in collection | query_vector: Vector, top_k: int | List[Result] |
| `WeaviateAdapter.create_schema()` | weaviate_adapter | Create Weaviate schema | schema: Schema | Status |
| `WeaviateAdapter.add_objects()` | weaviate_adapter | Add objects to Weaviate | objects: List[Object] | Status |
| `WeaviateAdapter.graphql_query()` | weaviate_adapter | Execute GraphQL queries | query: str | QueryResult |
| `QdrantAdapter.create_collection()` | qdrant_adapter | Create Qdrant collection | name: str, config: Dict | Collection |
| `QdrantAdapter.upsert_points()` | qdrant_adapter | Insert or update points | points: List[Point] | Status |
| `QdrantAdapter.search_points()` | qdrant_adapter | Search points with filters | query: Vector, filters: Dict | List[Result] |
| `NamespaceManager.create_namespace()` | namespace_manager | Create isolated namespace | name: str | Namespace |
| `NamespaceManager.set_access_control()` | namespace_manager | Set namespace permissions | namespace: str, permissions: Dict | Status |
| `NamespaceManager.list_namespaces()` | namespace_manager | List available namespaces | None | List[Namespace] |
| `MetadataStore.index_metadata()` | metadata_store | Index metadata for search | metadata: Dict | Status |
| `MetadataStore.filter_by_metadata()` | metadata_store | Filter results by metadata | filters: Dict | List[Result] |
| `MetadataStore.search_metadata()` | metadata_store | Search metadata content | query: str | List[Result] |
| `HybridSearch.combine_results()` | hybrid_search | Combine vector and metadata results | vector_results: List, metadata_results: List | List[Result] |
| `HybridSearch.rank_results()` | hybrid_search | Rank results using multiple criteria | results: List[Result] | List[RankedResult] |
| `HybridSearch.fuse_results()` | hybrid_search | Fuse results from different sources | results: List[List[Result]] | List[FusedResult] |
| `IndexOptimizer.optimize_index()` | index_optimizer | Optimize index performance | index: Index | OptimizedIndex |
| `IndexOptimizer.rebuild_index()` | index_optimizer | Rebuild index for better performance | index: Index | Index |
| `IndexOptimizer.get_performance_metrics()` | index_optimizer | Get index performance metrics | index: Index | Metrics |
| `VectorStoreManager.get_store_info()` | vector_store | Get store information | None | StoreInfo |
| `VectorStoreManager.backup_store()` | vector_store | Create store backup | backup_path: str | Status |
| `VectorStoreManager.restore_store()` | vector_store | Restore from backup | backup_path: str | Status |

#### 11. **Triple Store Functions** (`semantica.triple_store`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `BlazegraphAdapter.connect()` | blazegraph_adapter | Connect to Blazegraph instance | endpoint: str | Connection |
| `BlazegraphAdapter.execute_sparql()` | blazegraph_adapter | Execute SPARQL queries | query: str | QueryResult |
| `BlazegraphAdapter.bulk_load()` | blazegraph_adapter | Load triples in bulk | triples: List[Triple] | Status |
| `JenaAdapter.create_model()` | jena_adapter | Create and manage RDF models | None | Model |
| `JenaAdapter.add_triples()` | jena_adapter | Add triples to model | model: Model, triples: List[Triple] | Status |
| `JenaAdapter.run_inference()` | jena_adapter | Execute inference rules | model: Model | InferredModel |
| `RDF4JAdapter.create_repository()` | rdf4j_adapter | Create and configure repositories | config: Dict | Repository |
| `RDF4JAdapter.begin_transaction()` | rdf4j_adapter | Start transaction for batch operations | None | Transaction |
| `GraphDBAdapter.enable_reasoning()` | graphdb_adapter | Enable reasoning capabilities | config: Dict | Status |
| `GraphDBAdapter.visualize_graph()` | graphdb_adapter | Generate graph visualizations | query: str | Visualization |
| `VirtuosoAdapter.connect_cluster()` | virtuoso_adapter | Connect to Virtuoso cluster | cluster_config: Dict | Connection |
| `VirtuosoAdapter.optimize_queries()` | virtuoso_adapter | Optimize query performance | queries: List[str] | OptimizedQueries |
| `TripleManager.add_triple()` | triple_manager | Add single triple to store | triple: Triple | Status |
| `TripleManager.add_triples()` | triple_manager | Add multiple triples | triples: List[Triple] | Status |
| `TripleManager.delete_triple()` | triple_manager | Delete specific triple | triple: Triple | Status |
| `TripleManager.update_triple()` | triple_manager | Update existing triple | old_triple: Triple, new_triple: Triple | Status |
| `QueryEngine.execute_sparql()` | query_engine | Execute SPARQL queries | query: str | QueryResult |
| `QueryEngine.optimize_query()` | query_engine | Optimize query for performance | query: str | OptimizedQuery |
| `QueryEngine.format_results()` | query_engine | Format query results | results: QueryResult, format: str | FormattedResults |
| `BulkLoader.load_file()` | bulk_loader | Load triples from file | file_path: str | Status |
| `BulkLoader.create_indexes()` | bulk_loader | Create database indexes | None | Status |
| `BulkLoader.monitor_progress()` | bulk_loader | Monitor loading progress | None | Progress |
| `TripleStoreManager.get_store_info()` | triple_store | Get store statistics and status | None | StoreInfo |
| `TripleStoreManager.backup_store()` | triple_store | Create backup of store | backup_path: str | Status |
| `TripleStoreManager.restore_store()` | triple_store | Restore from backup | backup_path: str | Status |

#### 12. **Embeddings Functions** (`semantica.embeddings`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `TextEmbedder.embed_text()` | text_embedder | Generate text embeddings | text: str | Vector |
| `TextEmbedder.embed_sentence()` | text_embedder | Generate sentence-level embeddings | sentence: str | Vector |
| `TextEmbedder.embed_document()` | text_embedder | Generate document-level embeddings | document: str | Vector |
| `ImageEmbedder.embed_image()` | image_embedder | Generate image embeddings | image_path: str | Vector |
| `ImageEmbedder.extract_features()` | image_embedder | Extract visual features | image_path: str | Features |
| `ImageEmbedder.embed_batch()` | image_embedder | Process multiple images | image_paths: List[str] | List[Vector] |
| `AudioEmbedder.embed_audio()` | audio_embedder | Generate audio embeddings | audio_path: str | Vector |
| `AudioEmbedder.extract_audio_features()` | audio_embedder | Extract audio features | audio_path: str | Features |
| `MultimodalEmbedder.fuse_embeddings()` | multimodal_embedder | Fuse multiple modality embeddings | embeddings: List[Vector] | FusedVector |
| `MultimodalEmbedder.align_modalities()` | multimodal_embedder | Align different modality representations | modalities: List[Vector] | AlignedVectors |
| `ContextManager.set_window_size()` | context_manager | Set context window size | size: int | None |
| `ContextManager.apply_sliding_window()` | context_manager | Apply sliding window approach | text: str | List[Window] |
| `ContextManager.manage_attention()` | context_manager | Manage attention mechanisms | config: Dict | AttentionWeights |
| `PoolingStrategies.mean_pooling()` | pooling_strategies | Apply mean pooling strategy | vectors: List[Vector] | Vector |
| `PoolingStrategies.max_pooling()` | pooling_strategies | Apply max pooling strategy | vectors: List[Vector] | Vector |
| `PoolingStrategies.attention_pooling()` | pooling_strategies | Apply attention-based pooling | vectors: List[Vector], weights: List[float] | Vector |
| `ProviderAdapter.connect_openai()` | provider_adapter | Connect to OpenAI embedding API | api_key: str | Connection |
| `ProviderAdapter.connect_bge()` | provider_adapter | Connect to BGE embedding service | endpoint: str | Connection |
| `ProviderAdapter.connect_llama()` | provider_adapter | Connect to Llama embedding model | model_path: str | Connection |
| `ProviderAdapter.load_custom_model()` | provider_adapter | Load custom embedding model | model_config: Dict | Model |
| `EmbeddingOptimizer.optimize_dimensions()` | embedding_optimizer | Optimize embedding dimensions | vectors: List[Vector], target_dim: int | OptimizedVectors |
| `EmbeddingOptimizer.apply_clustering()` | embedding_optimizer | Apply clustering to embeddings | vectors: List[Vector] | ClusterResults |
| `EmbeddingOptimizer.calculate_similarity()` | embedding_optimizer | Calculate embedding similarities | vector1: Vector, vector2: Vector | float |
| `SemanticEmbedder.generate_embeddings()` | embeddings | Generate embeddings for input | input_data: Any | List[Vector] |
| `SemanticEmbedder.batch_process()` | embeddings | Process multiple inputs in batch | inputs: List[Any] | List[Vector] |
| `SemanticEmbedder.get_embedding_stats()` | embeddings | Get embedding statistics | None | Stats |

#### 13. **RAG System Functions** (`semantica.qa_rag`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `RAGManager.process_question()` | qa_rag | Process user question | question: str | Answer |
| `RAGManager.get_answer()` | qa_rag | Get RAG-generated answer | question: str | Answer |
| `RAGManager.evaluate_performance()` | qa_rag | Evaluate RAG performance | test_data: List[Question] | PerformanceMetrics |
| `SemanticChunker.chunk_text()` | semantic_chunker | Create semantic chunks with context | text: str | List[Chunk] |
| `SemanticChunker.optimize_chunks()` | semantic_chunker | Optimize chunk size and overlap | chunks: List[Chunk] | List[OptimizedChunk] |
| `SemanticChunker.merge_chunks()` | semantic_chunker | Merge related chunks when needed | chunks: List[Chunk] | List[MergedChunk] |
| `PromptTemplates.get_template()` | prompt_templates | Get RAG prompt template | template_name: str | Template |
| `PromptTemplates.format_question()` | prompt_templates | Format question for retrieval | question: str | FormattedQuestion |
| `PromptTemplates.inject_context()` | prompt_templates | Inject retrieved context into prompt | question: str, context: str | Prompt |
| `RetrievalPolicies.set_strategy()` | retrieval_policies | Set retrieval strategy | strategy: str | None |
| `RetrievalPolicies.rank_results()` | retrieval_policies | Rank retrieval results | results: List[Result] | List[RankedResult] |
| `RetrievalPolicies.filter_results()` | retrieval_policies | Filter results by criteria | results: List[Result], criteria: Dict | List[FilteredResult] |
| `AnswerBuilder.construct_answer()` | answer_builder | Construct answer from retrieved context | context: List[Chunk], question: str | Answer |
| `AnswerBuilder.integrate_context()` | answer_builder | Integrate multiple context sources | contexts: List[Context] | IntegratedContext |
| `AnswerBuilder.attribute_sources()` | answer_builder | Attribute answer to source documents | answer: Answer | AttributedAnswer |
| `ProvenanceTracker.track_sources()` | provenance_tracker | Track information sources | answer: Answer | List[Source] |
| `ProvenanceTracker.calculate_confidence()` | provenance_tracker | Calculate answer confidence | answer: Answer | float |
| `ProvenanceTracker.link_evidence()` | provenance_tracker | Link answer to supporting evidence | answer: Answer | List[Evidence] |
| `AnswerValidator.validate_answer()` | answer_validator | Validate answer accuracy | answer: Answer | ValidationResult |
| `AnswerValidator.fact_check()` | answer_validator | Perform fact checking | answer: Answer | FactCheckResult |
| `AnswerValidator.verify_consistency()` | answer_validator | Verify answer consistency | answer: Answer | ConsistencyResult |
| `RAGOptimizer.optimize_retrieval()` | rag_optimizer | Optimize retrieval performance | config: Dict | OptimizedConfig |
| `RAGOptimizer.enhance_queries()` | rag_optimizer | Enhance user queries | query: str | EnhancedQuery |
| `RAGOptimizer.improve_ranking()` | rag_optimizer | Improve result ranking | results: List[Result] | List[ImprovedResult] |
| `ConversationManager.start_conversation()` | conversation_manager | Start new conversation | None | Conversation |
| `ConversationManager.add_context()` | conversation_manager | Add context to conversation | conversation: Conversation, context: str | None |
| `ConversationManager.get_history()` | conversation_manager | Get conversation history | conversation: Conversation | List[Message] |

#### 14. **Reasoning Engine Functions** (`semantica.reasoning`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `InferenceEngine.add_rule()` | inference_engine | Add inference rule to engine | rule: Rule | Status |
| `InferenceEngine.execute_rules()` | inference_engine | Execute inference rules | None | List[Inference] |
| `InferenceEngine.forward_chain()` | inference_engine | Perform forward chaining | None | List[Inference] |
| `InferenceEngine.backward_chain()` | inference_engine | Perform backward chaining | goal: Goal | List[Inference] |
| `InferenceEngine.resolve_conflicts()` | inference_engine | Resolve rule conflicts | conflicts: List[Conflict] | Resolution |
| `SPARQLReasoner.expand_query()` | sparql_reasoner | Expand SPARQL query with reasoning | query: str | ExpandedQuery |
| `SPARQLReasoner.infer_results()` | sparql_reasoner | Infer additional results | query_results: QueryResult | InferredResults |
| `SPARQLReasoner.apply_reasoning()` | sparql_reasoner | Apply reasoning to query results | query: str, results: QueryResult | ReasonedResults |
| `ReteEngine.compile_rules()` | rete_engine | Compile rules into Rete network | rules: List[Rule] | ReteNetwork |
| `ReteEngine.match_patterns()` | rete_engine | Match patterns using Rete algorithm | facts: List[Fact] | List[Match] |
| `ReteEngine.execute_matches()` | rete_engine | Execute matched rules | matches: List[Match] | List[Inference] |
| `AbductiveReasoner.generate_hypotheses()` | abductive_reasoner | Generate explanatory hypotheses | observations: List[Observation] | List[Hypothesis] |
| `AbductiveReasoner.find_explanations()` | abductive_reasoner | Find explanations for observations | observations: List[Observation] | List[Explanation] |
| `AbductiveReasoner.rank_hypotheses()` | abductive_reasoner | Rank hypotheses by plausibility | hypotheses: List[Hypothesis] | List[RankedHypothesis] |
| `DeductiveReasoner.apply_logic()` | deductive_reasoner | Apply logical inference rules | premises: List[Premise] | List[Conclusion] |
| `DeductiveReasoner.prove_theorem()` | deductive_reasoner | Prove logical theorems | theorem: Theorem | Proof |
| `DeductiveReasoner.validate_argument()` | deductive_reasoner | Validate logical arguments | argument: Argument | ValidationResult |
| `RuleManager.define_rule()` | rule_manager | Define new inference rule | rule_definition: str | Rule |
| `RuleManager.validate_rule()` | rule_manager | Validate rule syntax and logic | rule: Rule | ValidationResult |
| `RuleManager.track_execution()` | rule_manager | Track rule execution history | rule: Rule | ExecutionHistory |
| `ReasoningValidator.validate_reasoning()` | reasoning_validator | Validate reasoning process | reasoning: Reasoning | ValidationResult |
| `ReasoningValidator.check_consistency()` | reasoning_validator | Check reasoning consistency | reasoning: Reasoning | ConsistencyResult |
| `ReasoningValidator.detect_errors()` | reasoning_validator | Detect reasoning errors | reasoning: Reasoning | List[Error] |
| `ExplanationGenerator.generate_explanation()` | explanation_generator | Generate reasoning explanation | reasoning: Reasoning | Explanation |
| `ExplanationGenerator.show_reasoning_path()` | explanation_generator | Show reasoning path | reasoning: Reasoning | ReasoningPath |
| `ExplanationGenerator.justify_conclusion()` | explanation_generator | Justify reasoning conclusion | conclusion: Conclusion | Justification |
| `ReasoningManager.run_reasoning()` | reasoning | Run complete reasoning process | input_data: Any | ReasoningResult |
| `ReasoningManager.get_reasoning_results()` | reasoning | Get reasoning results | reasoning_id: str | ReasoningResult |
| `ReasoningManager.export_reasoning()` | reasoning | Export reasoning process | reasoning: Reasoning, format: str | str |

#### 15. **Multi-Agent System Functions** (`semantica.agents`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `AgentManager.register_agent()` | agent_manager | Register new agent | agent_config: Dict | Agent |
| `AgentManager.start_agent()` | agent_manager | Start agent execution | agent: Agent | Status |
| `AgentManager.stop_agent()` | agent_manager | Stop agent execution | agent_id: str | Status |
| `AgentManager.monitor_agent()` | agent_manager | Monitor agent status | agent_id: str | AgentStatus |
| `OrchestrationEngine.coordinate_agents()` | orchestration_engine | Coordinate multiple agents | agents: List[Agent] | Coordination |
| `OrchestrationEngine.distribute_tasks()` | orchestration_engine | Distribute tasks among agents | tasks: List[Task], agents: List[Agent] | TaskDistribution |
| `OrchestrationEngine.manage_workflows()` | orchestration_engine | Manage agent workflows | workflow: Workflow | WorkflowStatus |
| `ToolRegistry.register_tool()` | tool_registry | Register tool for agent use | tool: Tool | Status |
| `ToolRegistry.discover_tools()` | tool_registry | Discover available tools | None | List[Tool] |
| `ToolRegistry.get_tool()` | tool_registry | Get specific tool | tool_name: str | Tool |
| `CostTracker.monitor_costs()` | cost_tracker | Monitor agent execution costs | agent_id: str | CostMetrics |
| `CostTracker.set_budget()` | cost_tracker | Set cost budget limits | budget: float | Status |
| `CostTracker.optimize_resources()` | cost_tracker | Optimize resource usage | usage_data: Dict | OptimizationPlan |
| `SandboxManager.create_sandbox()` | sandbox_manager | Create agent sandbox | config: Dict | Sandbox |
| `SandboxManager.isolate_agent()` | sandbox_manager | Isolate agent execution | agent: Agent | IsolationStatus |
| `SandboxManager.set_resource_limits()` | sandbox_manager | Set resource limits | limits: Dict | Status |
| `WorkflowEngine.define_workflow()` | workflow_engine | Define agent workflow | workflow_definition: Dict | Workflow |
| `WorkflowEngine.execute_workflow()` | workflow_engine | Execute defined workflow | workflow: Workflow | WorkflowResult |
| `WorkflowEngine.monitor_progress()` | workflow_engine | Monitor workflow progress | workflow_id: str | Progress |
| `AgentCommunication.send_message()` | agent_communication | Send message between agents | from_agent: str, to_agent: str, message: Message | Status |
| `AgentCommunication.route_message()` | agent_communication | Route message to appropriate agent | message: Message | RoutingResult |
| `AgentCommunication.manage_protocols()` | agent_communication | Manage communication protocols | protocols: List[Protocol] | Status |
| `PolicyEnforcer.enforce_policy()` | policy_enforcer | Enforce access policies | agent: Agent, resource: Resource | EnforcementResult |
| `PolicyEnforcer.check_compliance()` | policy_enforcer | Check policy compliance | agent: Agent | ComplianceResult |
| `PolicyEnforcer.set_permissions()` | policy_enforcer | Set agent permissions | agent: Agent, permissions: List[Permission] | Status |
| `AgentAnalytics.analyze_performance()` | agent_analytics | Analyze agent performance | agent_id: str | PerformanceMetrics |
| `AgentAnalytics.analyze_behavior()` | agent_analytics | Analyze agent behavior patterns | agent_id: str | BehaviorAnalysis |
| `AgentAnalytics.optimize_agents()` | agent_analytics | Optimize agent performance | agents: List[Agent] | OptimizationPlan |
| `MultiAgentManager.create_team()` | multi_agent_manager | Create agent team | team_config: Dict | Team |
| `MultiAgentManager.orchestrate_workflow()` | multi_agent_manager | Orchestrate team workflow | team: Team, workflow: Workflow | WorkflowResult |
| `MultiAgentManager.get_team_status()` | multi_agent_manager | Get team execution status | team_id: str | TeamStatus |

#### 16. **Domain Specialization Functions** (`semantica.domains`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `FinanceSpecialist.analyze_financial_data()` | finance | Analyze financial documents and data | data: FinancialData | Analysis |
| `FinanceSpecialist.extract_financial_entities()` | finance | Extract financial entities and metrics | text: str | List[FinancialEntity] |
| `FinanceSpecialist.calculate_ratios()` | finance | Calculate financial ratios | data: FinancialData | List[Ratio] |
| `HealthcareSpecialist.process_medical_records()` | healthcare | Process medical records and documents | records: MedicalRecords | ProcessedRecords |
| `HealthcareSpecialist.extract_medical_entities()` | healthcare | Extract medical entities and concepts | text: str | List[MedicalEntity] |
| `HealthcareSpecialist.analyze_drug_interactions()` | healthcare | Analyze drug interaction patterns | drugs: List[Drug] | List[Interaction] |
| `LegalSpecialist.analyze_legal_documents()` | legal | Analyze legal documents and contracts | documents: LegalDocuments | Analysis |
| `LegalSpecialist.extract_legal_entities()` | legal | Extract legal entities and clauses | text: str | List[LegalEntity] |
| `LegalSpecialist.identify_risks()` | legal | Identify legal risks and compliance issues | document: LegalDocument | List[Risk] |
| `ScientificSpecialist.process_research_papers()` | scientific | Process scientific research papers | papers: ResearchPapers | ProcessedPapers |
| `ScientificSpecialist.extract_scientific_entities()` | scientific | Extract scientific entities and concepts | text: str | List[ScientificEntity] |
| `ScientificSpecialist.analyze_citations()` | scientific | Analyze citation networks and patterns | papers: List[Paper] | CitationAnalysis |
| `DomainManager.register_domain()` | domain_manager | Register new domain specialization | domain_config: Dict | Domain |
| `DomainManager.get_domain_processor()` | domain_manager | Get domain-specific processor | domain: str | Processor |
| `DomainManager.list_domains()` | domain_manager | List available domains | None | List[Domain] |
| `DomainValidator.validate_domain_data()` | domain_validator | Validate domain-specific data | data: Any, domain: str | ValidationResult |
| `DomainValidator.check_compliance()` | domain_validator | Check domain compliance | data: Any, domain: str | ComplianceResult |
| `DomainOptimizer.optimize_for_domain()` | domain_optimizer | Optimize processing for specific domain | config: Dict, domain: str | OptimizedConfig |
| `DomainOptimizer.adapt_models()` | domain_optimizer | Adapt models for domain requirements | models: List[Model], domain: str | AdaptedModels |

#### 17. **User Interface Functions** (`semantica.ui`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `WebInterface.start_server()` | web_interface | Start web interface server | config: Dict | Server |
| `WebInterface.create_dashboard()` | web_interface | Create interactive dashboard | dashboard_config: Dict | Dashboard |
| `WebInterface.add_widget()` | web_interface | Add widget to dashboard | widget: Widget | Status |
| `CLIInterface.create_command()` | cli_interface | Create CLI command | command_config: Dict | Command |
| `CLIInterface.add_subcommand()` | cli_interface | Add subcommand to CLI | subcommand: SubCommand | Status |
| `CLIInterface.setup_help()` | cli_interface | Setup command help and documentation | command: Command | Status |
| `APIInterface.create_endpoint()` | api_interface | Create REST API endpoint | endpoint_config: Dict | Endpoint |
| `APIInterface.add_middleware()` | api_interface | Add middleware to API | middleware: Middleware | Status |
| `APIInterface.generate_docs()` | api_interface | Generate API documentation | None | Documentation |
| `VisualizationEngine.create_chart()` | visualization_engine | Create data visualization chart | chart_config: Dict | Chart |
| `VisualizationEngine.create_graph()` | visualization_engine | Create knowledge graph visualization | graph: Graph | GraphViz |
| `VisualizationEngine.export_visualization()` | visualization_engine | Export visualization to file | visualization: Visualization, format: str | Status |
| `UIThemeManager.set_theme()` | ui_theme_manager | Set UI theme and styling | theme: Theme | Status |
| `UIThemeManager.customize_colors()` | ui_theme_manager | Customize color scheme | colors: ColorScheme | Status |
| `UIThemeManager.apply_responsive_design()` | ui_theme_manager | Apply responsive design | breakpoints: List[Breakpoint] | Status |
| `UserManager.create_user()` | user_manager | Create new user account | user_data: Dict | User |
| `UserManager.authenticate_user()` | user_manager | Authenticate user login | credentials: Credentials | AuthResult |
| `UserManager.set_permissions()` | user_manager | Set user permissions | user: User, permissions: List[Permission] | Status |
| `SessionManager.create_session()` | session_manager | Create user session | user: User | Session |
| `SessionManager.validate_session()` | session_manager | Validate session token | token: str | ValidationResult |
| `SessionManager.refresh_session()` | session_manager | Refresh session token | session: Session | NewSession |
| `UIComponentManager.register_component()` | ui_component_manager | Register UI component | component: Component | Status |
| `UIComponentManager.get_component()` | ui_component_manager | Get component by name | name: str | Component |
| `UIComponentManager.render_component()` | ui_component_manager | Render component with data | component: Component, data: Any | RenderedComponent |

#### 18. **Operations Functions** (`semantica.ops`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `DeploymentManager.deploy_service()` | deployment_manager | Deploy service to production | service_config: Dict | Deployment |
| `DeploymentManager.rollback_deployment()` | deployment_manager | Rollback to previous version | deployment_id: str | Status |
| `DeploymentManager.scale_service()` | deployment_manager | Scale service instances | service: Service, instances: int | Status |
| `MonitoringManager.setup_monitoring()` | monitoring_manager | Setup system monitoring | config: Dict | Monitoring |
| `MonitoringManager.create_alert()` | monitoring_manager | Create monitoring alert | alert_config: Dict | Alert |
| `MonitoringManager.get_metrics()` | monitoring_manager | Get system metrics | time_range: TimeRange | Metrics |
| `LoggingManager.configure_logging()` | logging_manager | Configure logging system | config: Dict | Status |
| `LoggingManager.create_log_handler()` | logging_manager | Create custom log handler | handler_config: Dict | LogHandler |
| `LoggingManager.analyze_logs()` | logging_manager | Analyze log patterns | logs: List[Log] | LogAnalysis |
| `BackupManager.create_backup()` | backup_manager | Create system backup | backup_config: Dict | Backup |
| `BackupManager.restore_backup()` | backup_manager | Restore from backup | backup_id: str | Status |
| `BackupManager.schedule_backup()` | backup_manager | Schedule automatic backups | schedule: Schedule | Status |
| `SecurityManager.audit_security()` | security_manager | Perform security audit | None | AuditResult |
| `SecurityManager.scan_vulnerabilities()` | security_manager | Scan for security vulnerabilities | None | VulnerabilityReport |
| `SecurityManager.update_policies()` | security_manager | Update security policies | policies: List[Policy] | Status |
| `PerformanceManager.optimize_performance()` | performance_manager | Optimize system performance | config: Dict | OptimizationResult |
| `PerformanceManager.benchmark_system()` | performance_manager | Benchmark system performance | None | BenchmarkResult |
| `PerformanceManager.profile_application()` | performance_manager | Profile application performance | app: Application | ProfileResult |
| `ResourceManager.allocate_resources()` | resource_manager | Allocate system resources | resource_config: Dict | ResourceAllocation |
| `ResourceManager.monitor_usage()` | resource_manager | Monitor resource usage | None | UsageMetrics |
| `ResourceManager.optimize_allocation()` | resource_manager | Optimize resource allocation | usage_data: UsageData | OptimizationPlan |
| `OpsManager.deploy_infrastructure()` | ops_manager | Deploy infrastructure components | infra_config: Dict | Infrastructure |
| `OpsManager.manage_services()` | ops_manager | Manage service lifecycle | services: List[Service] | ServiceStatus |
| `OpsManager.get_operational_status()` | ops_manager | Get operational status | None | OperationalStatus |

#### 19. **Monitoring Functions** (`semantica.monitoring`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `MetricsCollector.collect_metrics()` | metrics_collector | Collect system metrics | None | Metrics |
| `MetricsCollector.aggregate_metrics()` | metrics_collector | Aggregate metrics over time | time_range: TimeRange | AggregatedMetrics |
| `MetricsCollector.export_metrics()` | metrics_collector | Export metrics to external systems | metrics: Metrics, format: str | Status |
| `HealthChecker.check_health()` | health_checker | Check system health status | None | HealthStatus |
| `HealthChecker.run_diagnostics()` | health_checker | Run system diagnostics | None | DiagnosticReport |
| `HealthChecker.validate_components()` | health_checker | Validate component health | components: List[Component] | ValidationResult |
| `AlertManager.create_alert()` | alert_manager | Create monitoring alert | alert_config: Dict | Alert |
| `AlertManager.send_notification()` | alert_manager | Send alert notification | alert: Alert | Status |
| `AlertManager.escalate_alert()` | alert_manager | Escalate alert to higher level | alert: Alert | Escalation |
| `PerformanceProfiler.profile_system()` | performance_profiler | Profile system performance | None | Profile |
| `PerformanceProfiler.analyze_bottlenecks()` | performance_profiler | Analyze performance bottlenecks | profile: Profile | BottleneckAnalysis |
| `PerformanceProfiler.optimize_performance()` | performance_profiler | Optimize based on profile | profile: Profile | OptimizationPlan |
| `LogAnalyzer.analyze_logs()` | log_analyzer | Analyze log files for patterns | logs: List[Log] | LogAnalysis |
| `LogAnalyzer.detect_anomalies()` | log_analyzer | Detect anomalous log patterns | logs: List[Log] | List[Anomaly] |
| `LogAnalyzer.generate_report()` | log_analyzer | Generate log analysis report | analysis: LogAnalysis | Report |
| `MonitoringDashboard.create_dashboard()` | monitoring_dashboard | Create monitoring dashboard | config: Dict | Dashboard |
| `MonitoringDashboard.add_widget()` | monitoring_dashboard | Add widget to dashboard | widget: Widget | Status |
| `MonitoringDashboard.refresh_data()` | monitoring_dashboard | Refresh dashboard data | None | Status |
| `MonitoringManager.setup_monitoring()` | monitoring | Setup complete monitoring system | config: Dict | MonitoringSystem |
| `MonitoringManager.get_status()` | monitoring | Get monitoring system status | None | Status |
| `MonitoringManager.configure_alerts()` | monitoring | Configure alert rules | alert_rules: List[AlertRule] | Status |

#### 20. **Quality Assurance Functions** (`semantica.quality`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `QAEngine.run_quality_tests()` | qa_engine | Run quality assurance tests | test_config: Dict | TestResults |
| `QAEngine.validate_data_quality()` | qa_engine | Validate data quality metrics | data: Any | QualityScore |
| `QAEngine.check_consistency()` | qa_engine | Check data consistency | data: Any | ConsistencyReport |
| `ValidationEngine.validate_schema()` | validation_engine | Validate data against schema | data: Any, schema: Schema | ValidationResult |
| `ValidationEngine.validate_format()` | validation_engine | Validate data format | data: Any, format: Format | ValidationResult |
| `ValidationEngine.validate_business_rules()` | validation_engine | Validate business rules | data: Any, rules: List[Rule] | ValidationResult |
| `TestRunner.execute_tests()` | test_runner | Execute test suite | tests: List[Test] | TestResults |
| `TestRunner.generate_report()` | test_runner | Generate test report | results: TestResults | TestReport |
| `TestRunner.analyze_coverage()` | test_runner | Analyze test coverage | results: TestResults | CoverageReport |
| `QualityMetrics.calculate_accuracy()` | quality_metrics | Calculate accuracy metrics | predictions: List[Prediction], ground_truth: List[Truth] | AccuracyScore |
| `QualityMetrics.calculate_precision()` | quality_metrics | Calculate precision metrics | predictions: List[Prediction], ground_truth: List[Truth] | PrecisionScore |
| `QualityMetrics.calculate_recall()` | quality_metrics | Calculate recall metrics | predictions: List[Prediction], ground_truth: List[Truth] | RecallScore |
| `DataValidator.validate_integrity()` | data_validator | Validate data integrity | data: Any | IntegrityReport |
| `DataValidator.check_completeness()` | data_validator | Check data completeness | data: Any | CompletenessReport |
| `DataValidator.verify_accuracy()` | data_validator | Verify data accuracy | data: Any | AccuracyReport |
| `QualityManager.assess_quality()` | quality_manager | Assess overall quality | data: Any | QualityAssessment |
| `QualityManager.improve_quality()` | quality_manager | Improve data quality | data: Any, issues: List[Issue] | ImprovedData |
| `QualityManager.track_quality_trends()` | quality_manager | Track quality trends over time | historical_data: List[Data] | QualityTrends |

#### 21. **Security Functions** (`semantica.security`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `AccessControl.check_permissions()` | access_control | Check user permissions | user: User, resource: Resource | PermissionResult |
| `AccessControl.grant_access()` | access_control | Grant access to resource | user: User, resource: Resource, permissions: List[Permission] | Status |
| `AccessControl.revoke_access()` | access_control | Revoke access to resource | user: User, resource: Resource | Status |
| `DataMasking.mask_sensitive_data()` | data_masking | Mask sensitive data fields | data: Any, fields: List[str] | MaskedData |
| `DataMasking.anonymize_data()` | data_masking | Anonymize personal data | data: Any | AnonymizedData |
| `DataMasking.encrypt_data()` | data_masking | Encrypt sensitive data | data: Any, key: str | EncryptedData |
| `EncryptionManager.encrypt_file()` | encryption_manager | Encrypt file with specified algorithm | file_path: str, algorithm: str | Status |
| `EncryptionManager.decrypt_file()` | encryption_manager | Decrypt file | file_path: str, key: str | Status |
| `EncryptionManager.generate_key()` | encryption_manager | Generate encryption key | algorithm: str | Key |
| `AuthenticationManager.authenticate_user()` | authentication_manager | Authenticate user credentials | credentials: Credentials | AuthResult |
| `AuthenticationManager.create_session()` | authentication_manager | Create authenticated session | user: User | Session |
| `AuthenticationManager.validate_token()` | authentication_manager | Validate authentication token | token: str | ValidationResult |
| `AuditLogger.log_access()` | audit_logger | Log access attempts | access: Access | Status |
| `AuditLogger.log_data_changes()` | audit_logger | Log data modifications | changes: List[Change] | Status |
| `AuditLogger.generate_audit_report()` | audit_logger | Generate audit report | time_range: TimeRange | AuditReport |
| `SecurityScanner.scan_vulnerabilities()` | security_scanner | Scan for security vulnerabilities | None | VulnerabilityReport |
| `SecurityScanner.check_compliance()` | security_scanner | Check security compliance | None | ComplianceReport |
| `SecurityScanner.assess_risks()` | security_scanner | Assess security risks | None | RiskAssessment |
| `SecurityManager.implement_security()` | security | Implement security measures | config: Dict | SecurityStatus |
| `SecurityManager.monitor_threats()` | security | Monitor security threats | None | ThreatReport |
| `SecurityManager.respond_to_incident()` | security | Respond to security incident | incident: Incident | ResponsePlan |

#### 22. **CLI Tools Functions** (`semantica.cli`)

| Function | Module | Description | Parameters | Returns |
|----------|--------|-------------|------------|---------|
| `IngestionCLI.ingest_data()` | ingestion_cli | Ingest data from command line | source: str, config: Dict | Status |
| `IngestionCLI.resume_ingestion()` | ingestion_cli | Resume interrupted ingestion | token: str | Status |
| `IngestionCLI.monitor_progress()` | ingestion_cli | Monitor ingestion progress | None | Progress |
| `KBBuilderCLI.build_knowledge_base()` | kb_builder_cli | Build knowledge base from CLI | sources: List[str], config: Dict | Status |
| `KBBuilderCLI.export_kb()` | kb_builder_cli | Export knowledge base | kb: KnowledgeBase, format: str | Status |
| `KBBuilderCLI.validate_kb()` | kb_builder_cli | Validate knowledge base | kb: KnowledgeBase | ValidationResult |
| `PipelineCLI.create_pipeline()` | pipeline_cli | Create pipeline from CLI | config_file: str | Pipeline |
| `PipelineCLI.run_pipeline()` | pipeline_cli | Run pipeline from CLI | pipeline: Pipeline | Results |
| `PipelineCLI.monitor_pipeline()` | pipeline_cli | Monitor pipeline execution | pipeline_id: str | Status |
| `QueryCLI.execute_query()` | query_cli | Execute query from CLI | query: str, format: str | QueryResult |
| `QueryCLI.export_results()` | query_cli | Export query results | results: QueryResult, format: str | Status |
| `QueryCLI.optimize_query()` | query_cli | Optimize query performance | query: str | OptimizedQuery |
| `AdminCLI.manage_users()` | admin_cli | Manage user accounts | action: str, user_data: Dict | Status |
| `AdminCLI.configure_system()` | admin_cli | Configure system settings | config: Dict | Status |
| `AdminCLI.monitor_system()` | admin_cli | Monitor system status | None | SystemStatus |
| `CLIManager.setup_cli()` | cli_manager | Setup CLI environment | config: Dict | Status |
| `CLIManager.register_commands()` | cli_manager | Register CLI commands | commands: List[Command] | Status |
| `CLIManager.handle_errors()` | cli_manager | Handle CLI errors | error: Exception | ErrorResponse |

---

## 📊 Function Statistics

| Module | Total Functions | Core Functions | Utility Functions | Management Functions |
|--------|----------------|----------------|-------------------|---------------------|
| **Core Engine** | 13 | 6 | 4 | 3 |
| **Pipeline Builder** | 13 | 7 | 3 | 3 |
| **Data Ingestion** | 12 | 8 | 2 | 2 |
| **Document Parsing** | 16 | 12 | 2 | 2 |
| **Text Normalization** | 14 | 10 | 2 | 2 |
| **Text Chunking** | 14 | 8 | 4 | 2 |
| **Semantic Extraction** | 15 | 10 | 3 | 2 |
| **Ontology Generation** | 20 | 12 | 4 | 4 |
| **Knowledge Graph** | 25 | 15 | 6 | 4 |
| **Vector Store** | 25 | 15 | 6 | 4 |
| **Triple Store** | 25 | 15 | 6 | 4 |
| **Embeddings** | 20 | 12 | 4 | 4 |
| **RAG System** | 20 | 12 | 4 | 4 |
| **Reasoning Engine** | 25 | 15 | 6 | 4 |
| **Multi-Agent System** | 25 | 15 | 6 | 4 |
| **Domain Specialization** | 18 | 12 | 3 | 3 |
| **User Interface** | 20 | 12 | 4 | 4 |
| **Operations** | 20 | 12 | 4 | 4 |
| **Monitoring** | 20 | 12 | 4 | 4 |
| **Quality Assurance** | 18 | 12 | 3 | 3 |
| **Security** | 20 | 12 | 4 | 4 |
| **CLI Tools** | 18 | 12 | 3 | 3 |

**Total: 22 Modules, 450+ Functions**

---

## 📥 Import Reference

### Complete Import Guide

```python
# =============================================================================
# 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
```python
# 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
```python
# 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.*