# 🧩 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. [Knowledge Graph Quality Assurance](#knowledge-graph-quality-assurance)
7. [Complete Module Index](#complete-module-index)
8. [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

#### **Imports:**
```python
from semantica.ingest import FileIngestor, WebIngestor, FeedIngestor
from semantica.ingest.file import LocalFileHandler, S3Handler, GCSHandler, AzureHandler
from semantica.ingest.web import WebScraper, SitemapCrawler, JavaScriptRenderer
from semantica.ingest.feed import RSSParser, AtomParser, SocialMediaAPI
from semantica.ingest.stream import WebSocketHandler, MessageQueueHandler, KafkaStream
from semantica.ingest.repo import GitHandler, PackageManagerHandler
from semantica.ingest.email import IMAPHandler, ExchangeHandler, GmailAPIHandler
from semantica.ingest.db_export import DatabaseExporter, SQLQueryHandler, ETLProcessor
```

#### **Main Functions:**
```python
# File ingestion
file_ingestor = FileIngestor()
files = file_ingestor.scan_directory("documents/", recursive=True)
formats = file_ingestor.detect_format("document.pdf")
metadata = file_ingestor.extract_metadata("document.pdf")
content = file_ingestor.ingest_file("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)
sitemap = web_ingestor.parse_sitemap("https://example.com/sitemap.xml")

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

#### **Submodules with Functions:**

**File Handler (`semantica.ingest.file`):**
```python
from semantica.ingest.file import LocalFileHandler, S3Handler, GCSHandler, AzureHandler

# Local file handling
local_handler = LocalFileHandler()
files = local_handler.scan_directory("documents/", patterns=["*.pdf", "*.docx"])
file_info = local_handler.get_file_info("document.pdf")
content = local_handler.read_file("document.pdf", encoding="utf-8")

# S3 handling
s3_handler = S3Handler(bucket="my-bucket", region="us-east-1")
s3_files = s3_handler.list_objects(prefix="documents/")
s3_content = s3_handler.download_object("documents/file.pdf")
s3_handler.upload_object("local_file.pdf", "documents/remote_file.pdf")

# Google Cloud Storage
gcs_handler = GCSHandler(project="my-project", bucket="my-bucket")
gcs_files = gcs_handler.list_blobs(prefix="documents/")
gcs_content = gcs_handler.download_blob("documents/file.pdf")

# Azure Blob Storage
azure_handler = AzureHandler(account_name="myaccount", container="documents")
azure_files = azure_handler.list_blobs()
azure_content = azure_handler.download_blob("file.pdf")
```

**Web Scraper (`semantica.ingest.web`):**
```python
from semantica.ingest.web import WebScraper, SitemapCrawler, JavaScriptRenderer

# Web scraping
scraper = WebScraper(respect_robots=True, delay=1.0)
content = scraper.scrape_url("https://example.com")
links = scraper.extract_links(content)
images = scraper.extract_images(content)
text = scraper.extract_text(content)

# Sitemap crawling
sitemap_crawler = SitemapCrawler()
urls = sitemap_crawler.parse_sitemap("https://example.com/sitemap.xml")
sitemap_crawler.crawl_sitemap_urls(urls, max_pages=100)

# JavaScript rendering
js_renderer = JavaScriptRenderer(headless=True)
rendered_content = js_renderer.render_page("https://spa-example.com")
js_renderer.wait_for_element("div.content", timeout=10)
```

**Feed Parser (`semantica.ingest.feed`):**
```python
from semantica.ingest.feed import RSSParser, AtomParser, SocialMediaAPI

# RSS parsing
rss_parser = RSSParser()
rss_feed = rss_parser.parse_rss("https://example.com/feed.xml")
entries = rss_parser.get_entries(rss_feed)
rss_parser.save_entries(entries, "rss_entries.json")

# Atom parsing
atom_parser = AtomParser()
atom_feed = atom_parser.parse_atom("https://example.com/atom.xml")
atom_entries = atom_parser.get_entries(atom_feed)

# Social media APIs
social_api = SocialMediaAPI(platform="twitter", api_key="your_key")
tweets = social_api.fetch_posts(hashtag="#semantica", count=100)
social_api.export_posts(tweets, format="json")
```

**Stream Handler (`semantica.ingest.stream`):**
```python
from semantica.ingest.stream import WebSocketHandler, MessageQueueHandler, KafkaStream

# WebSocket streaming
ws_handler = WebSocketHandler(url="wss://example.com/stream")
ws_handler.connect()
messages = ws_handler.listen_for_messages(callback=process_message)
ws_handler.send_message({"type": "subscribe", "channel": "updates"})

# Message queue handling
mq_handler = MessageQueueHandler(queue_type="rabbitmq", host="localhost")
mq_handler.connect()
mq_handler.consume_messages(queue="data_queue", callback=process_message)
mq_handler.publish_message("data_queue", {"data": "new_content"})

# Kafka streaming
kafka_stream = KafkaStream(bootstrap_servers=["localhost:9092"])
kafka_stream.subscribe_topics(["data_topic"])
kafka_stream.consume_messages(callback=process_kafka_message)
kafka_stream.produce_message("data_topic", {"key": "value"})
```

**Repository Handler (`semantica.ingest.repo`):**
```python
from semantica.ingest.repo import GitHandler, PackageManagerHandler

# Git repository handling
git_handler = GitHandler()
git_handler.clone_repository("https://github.com/user/repo.git", "local_repo")
commits = git_handler.get_commits(since="2023-01-01")
files = git_handler.get_changed_files(commit_hash="abc123")
git_handler.checkout_branch("feature-branch")

# Package manager handling
pkg_handler = PackageManagerHandler(manager="npm")
packages = pkg_handler.list_packages()
pkg_handler.install_package("package-name", version="1.0.0")
pkg_info = pkg_handler.get_package_info("package-name")
```

**Email Handler (`semantica.ingest.email`):**
```python
from semantica.ingest.email import IMAPHandler, ExchangeHandler, GmailAPIHandler

# IMAP handling
imap_handler = IMAPHandler(server="imap.gmail.com", port=993)
imap_handler.connect(username="user@example.com", password="password")
emails = imap_handler.fetch_emails(folder="INBOX", since="2023-01-01")
attachments = imap_handler.download_attachments(email_id=123)

# Exchange handling
exchange_handler = ExchangeHandler(server="outlook.office365.com")
exchange_handler.connect(username="user@example.com", password="password")
exchange_emails = exchange_handler.get_emails(folder="Inbox")

# Gmail API handling
gmail_handler = GmailAPIHandler(credentials_file="credentials.json")
gmail_emails = gmail_handler.list_messages(query="is:unread")
gmail_handler.download_attachments(message_id="msg123")
```

**Database Export (`semantica.ingest.db_export`):**
```python
from semantica.ingest.db_export import DatabaseExporter, SQLQueryHandler, ETLProcessor

# Database export
db_exporter = DatabaseExporter(connection_string="postgresql://user:pass@localhost/db")
tables = db_exporter.export_table("users", output_format="csv")
db_exporter.export_schema(output_file="schema.sql")

# SQL query handling
sql_handler = SQLQueryHandler(connection_string="postgresql://user:pass@localhost/db")
results = sql_handler.execute_query("SELECT * FROM users WHERE active = true")
sql_handler.export_query_results(results, "active_users.csv")

# ETL processing
etl_processor = ETLProcessor()
etl_processor.extract_from_source("database", config=db_config)
etl_processor.transform_data(transform_rules=transformation_rules)
etl_processor.load_to_destination("data_warehouse", config=dw_config)
```

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

**Main Classes:** `PDFParser`, `DOCXParser`, `HTMLParser`, `ImageParser`

**Purpose:** Extract content from various document formats

#### **Imports:**
```python
from semantica.parse import PDFParser, DOCXParser, HTMLParser, ImageParser
from semantica.parse.pdf import PDFTextExtractor, PDFTableExtractor, PDFImageExtractor, PDFAnnotationExtractor
from semantica.parse.docx import DOCXTextExtractor, DOCXStyleExtractor, DOCXTrackChangesExtractor
from semantica.parse.pptx import PPTXSlideExtractor, PPTXNotesExtractor, PPTXMediaExtractor
from semantica.parse.excel import ExcelDataExtractor, ExcelFormulaExtractor, ExcelChartExtractor
from semantica.parse.html import HTMLDOMParser, HTMLMetadataExtractor, HTMLFormExtractor
from semantica.parse.images import OCRProcessor, ObjectDetector, EXIFExtractor
from semantica.parse.tables import TableDetector, TableStructureAnalyzer, TableDataExtractor
```

#### **Main Functions:**
```python
# 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")
pdf_annotations = pdf_parser.extract_annotations("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")
styles = docx_parser.extract_styles("document.docx")

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

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

#### **Submodules with Functions:**

**PDF Parser (`semantica.parse.pdf`):**
```python
from semantica.parse.pdf import PDFTextExtractor, PDFTableExtractor, PDFImageExtractor, PDFAnnotationExtractor

# Text extraction
text_extractor = PDFTextExtractor()
text = text_extractor.extract_text("document.pdf")
text_by_page = text_extractor.extract_text_by_page("document.pdf")
text_with_coordinates = text_extractor.extract_text_with_coordinates("document.pdf")

# Table extraction
table_extractor = PDFTableExtractor()
tables = table_extractor.extract_tables("document.pdf")
table_data = table_extractor.extract_table_data("document.pdf", page=1)
table_structure = table_extractor.analyze_table_structure("document.pdf")

# Image extraction
image_extractor = PDFImageExtractor()
images = image_extractor.extract_images("document.pdf")
image_metadata = image_extractor.get_image_metadata("document.pdf")
image_extractor.save_images("document.pdf", output_dir="extracted_images/")

# Annotation extraction
annotation_extractor = PDFAnnotationExtractor()
annotations = annotation_extractor.extract_annotations("document.pdf")
comments = annotation_extractor.extract_comments("document.pdf")
highlights = annotation_extractor.extract_highlights("document.pdf")
```

**DOCX Parser (`semantica.parse.docx`):**
```python
from semantica.parse.docx import DOCXTextExtractor, DOCXStyleExtractor, DOCXTrackChangesExtractor

# Text extraction
docx_text = DOCXTextExtractor()
text = docx_text.extract_text("document.docx")
paragraphs = docx_text.extract_paragraphs("document.docx")
headers_footers = docx_text.extract_headers_footers("document.docx")

# Style extraction
style_extractor = DOCXStyleExtractor()
styles = style_extractor.extract_styles("document.docx")
formatting = style_extractor.extract_formatting("document.docx")
tables = style_extractor.extract_table_styles("document.docx")

# Track changes extraction
track_changes = DOCXTrackChangesExtractor()
changes = track_changes.extract_changes("document.docx")
revisions = track_changes.extract_revisions("document.docx")
comments = track_changes.extract_comments("document.docx")
```

**PowerPoint Parser (`semantica.parse.pptx`):**
```python
from semantica.parse.pptx import PPTXSlideExtractor, PPTXNotesExtractor, PPTXMediaExtractor

# Slide extraction
slide_extractor = PPTXSlideExtractor()
slides = slide_extractor.extract_slides("presentation.pptx")
slide_text = slide_extractor.extract_slide_text("presentation.pptx", slide_number=1)
slide_layout = slide_extractor.extract_slide_layout("presentation.pptx")

# Notes extraction
notes_extractor = PPTXNotesExtractor()
notes = notes_extractor.extract_notes("presentation.pptx")
speaker_notes = notes_extractor.extract_speaker_notes("presentation.pptx")

# Media extraction
media_extractor = PPTXMediaExtractor()
images = media_extractor.extract_images("presentation.pptx")
videos = media_extractor.extract_videos("presentation.pptx")
audio = media_extractor.extract_audio("presentation.pptx")
```

**Excel Parser (`semantica.parse.excel`):**
```python
from semantica.parse.excel import ExcelDataExtractor, ExcelFormulaExtractor, ExcelChartExtractor

# Data extraction
excel_extractor = ExcelDataExtractor()
data = excel_extractor.extract_data("spreadsheet.xlsx", sheet_name="Sheet1")
all_sheets = excel_extractor.extract_all_sheets("spreadsheet.xlsx")
cell_values = excel_extractor.extract_cell_values("spreadsheet.xlsx", range="A1:C10")

# Formula extraction
formula_extractor = ExcelFormulaExtractor()
formulas = formula_extractor.extract_formulas("spreadsheet.xlsx")
formula_dependencies = formula_extractor.analyze_formula_dependencies("spreadsheet.xlsx")

# Chart extraction
chart_extractor = ExcelChartExtractor()
charts = chart_extractor.extract_charts("spreadsheet.xlsx")
chart_data = chart_extractor.extract_chart_data("spreadsheet.xlsx", chart_name="Chart1")
```

**HTML Parser (`semantica.parse.html`):**
```python
from semantica.parse.html import HTMLDOMParser, HTMLMetadataExtractor, HTMLFormExtractor

# DOM parsing
dom_parser = HTMLDOMParser()
dom_tree = dom_parser.parse_dom("https://example.com")
elements = dom_parser.find_elements(dom_tree, tag="div", class_name="content")
links = dom_parser.extract_links(dom_tree)
images = dom_parser.extract_images(dom_tree)

# Metadata extraction
metadata_extractor = HTMLMetadataExtractor()
title = metadata_extractor.extract_title(dom_tree)
description = metadata_extractor.extract_description(dom_tree)
keywords = metadata_extractor.extract_keywords(dom_tree)
og_data = metadata_extractor.extract_og_metadata(dom_tree)

# Form extraction
form_extractor = HTMLFormExtractor()
forms = form_extractor.extract_forms(dom_tree)
form_fields = form_extractor.extract_form_fields(dom_tree)
form_actions = form_extractor.extract_form_actions(dom_tree)
```

**Image Parser (`semantica.parse.images`):**
```python
from semantica.parse.images import OCRProcessor, ObjectDetector, EXIFExtractor

# OCR processing
ocr_processor = OCRProcessor()
text = ocr_processor.extract_text("image.png")
text_confidence = ocr_processor.extract_text_with_confidence("image.png")
text_by_region = ocr_processor.extract_text_by_region("image.png", regions=[(0,0,100,100)])

# Object detection
object_detector = ObjectDetector()
objects = object_detector.detect_objects("image.jpg")
faces = object_detector.detect_faces("image.jpg")
text_regions = object_detector.detect_text_regions("image.jpg")

# EXIF data extraction
exif_extractor = EXIFExtractor()
exif_data = exif_extractor.extract_exif("image.jpg")
camera_info = exif_extractor.extract_camera_info("image.jpg")
location_data = exif_extractor.extract_location("image.jpg")
```

**Table Parser (`semantica.parse.tables`):**
```python
from semantica.parse.tables import TableDetector, TableStructureAnalyzer, TableDataExtractor

# Table detection
table_detector = TableDetector()
tables = table_detector.detect_tables("document.pdf")
table_regions = table_detector.detect_table_regions("document.pdf")

# Structure analysis
structure_analyzer = TableStructureAnalyzer()
structure = structure_analyzer.analyze_structure("document.pdf", table_region)
headers = structure_analyzer.detect_headers("document.pdf", table_region)
rows_columns = structure_analyzer.detect_rows_columns("document.pdf", table_region)

# Data extraction
data_extractor = TableDataExtractor()
table_data = data_extractor.extract_data("document.pdf", table_region)
csv_data = data_extractor.extract_as_csv("document.pdf", table_region)
json_data = data_extractor.extract_as_json("document.pdf", table_region)
```

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

**Main Classes:** `TextCleaner`, `LanguageDetector`, `EntityNormalizer`

**Purpose:** Clean and normalize text data

#### **Imports:**
```python
from semantica.normalize import TextCleaner, LanguageDetector, EntityNormalizer
from semantica.normalize.text_cleaner import HTMLRemover, WhitespaceNormalizer, SpecialCharRemover
from semantica.normalize.language_detector import MultiLanguageDetector, LanguageConfidence, LanguageSupport
from semantica.normalize.encoding_handler import UTF8Converter, EncodingValidator, EncodingDetector
from semantica.normalize.entity_normalizer import EntityCanonicalizer, AcronymExpander, EntityStandardizer
from semantica.normalize.date_normalizer import DateFormatter, DateParser, DateValidator
from semantica.normalize.number_normalizer import NumberFormatter, NumberParser, CurrencyNormalizer
```

#### **Main Functions:**
```python
# Text cleaning
cleaner = TextCleaner()
clean_text = cleaner.remove_html(html_content)
normalized = cleaner.normalize_whitespace(text)
cleaned = cleaner.remove_special_chars(text)
unicode_normalized = cleaner.normalize_unicode(text)

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

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

#### **Submodules with Functions:**

**Text Cleaner (`semantica.normalize.text_cleaner`):**
```python
from semantica.normalize.text_cleaner import HTMLRemover, WhitespaceNormalizer, SpecialCharRemover

# HTML removal
html_remover = HTMLRemover()
clean_text = html_remover.remove_html("<p>Hello <b>world</b></p>")
clean_text = html_remover.remove_html_tags(html_content, keep_tags=["p", "br"])
clean_text = html_remover.remove_html_entities("&amp; &lt; &gt;")

# Whitespace normalization
whitespace_normalizer = WhitespaceNormalizer()
normalized = whitespace_normalizer.normalize_spaces("  multiple   spaces  ")
normalized = whitespace_normalizer.normalize_line_breaks(text)
normalized = whitespace_normalizer.remove_extra_whitespace(text)

# Special character removal
char_remover = SpecialCharRemover()
cleaned = char_remover.remove_special_chars("Text with @#$% symbols")
cleaned = char_remover.remove_control_chars(text)
cleaned = char_remover.remove_non_printable(text)
```

**Language Detector (`semantica.normalize.language_detector`):**
```python
from semantica.normalize.language_detector import MultiLanguageDetector, LanguageConfidence, LanguageSupport

# Multi-language detection
multi_detector = MultiLanguageDetector()
languages = multi_detector.detect_languages("Hello world. Bonjour le monde.")
primary_lang = multi_detector.get_primary_language(text)
language_confidence = multi_detector.get_confidence_scores(text)

# Language confidence
confidence_calculator = LanguageConfidence()
confidence = confidence_calculator.calculate_confidence(text, "en")
confidence_scores = confidence_calculator.get_all_confidence_scores(text)

# Language support
language_support = LanguageSupport()
supported = language_support.get_supported_languages()
is_supported = language_support.is_language_supported("en")
language_info = language_support.get_language_info("en")
```

**Encoding Handler (`semantica.normalize.encoding_handler`):**
```python
from semantica.normalize.encoding_handler import UTF8Converter, EncodingValidator, EncodingDetector

# UTF-8 conversion
utf8_converter = UTF8Converter()
utf8_text = utf8_converter.convert_to_utf8(text, source_encoding="latin1")
utf8_text = utf8_converter.ensure_utf8(text)
utf8_text = utf8_converter.fix_encoding_issues(text)

# Encoding validation
encoding_validator = EncodingValidator()
is_valid = encoding_validator.validate_encoding(text, "utf-8")
validation_report = encoding_validator.validate_text_encoding(text)
encoding_validator.fix_encoding_errors(text)

# Encoding detection
encoding_detector = EncodingDetector()
detected_encoding = encoding_detector.detect_encoding(text)
confidence = encoding_detector.get_detection_confidence()
possible_encodings = encoding_detector.get_possible_encodings(text)
```

**Entity Normalizer (`semantica.normalize.entity_normalizer`):**
```python
from semantica.normalize.entity_normalizer import EntityCanonicalizer, AcronymExpander, EntityStandardizer

# Entity canonicalization
canonicalizer = EntityCanonicalizer()
canonical = canonicalizer.canonicalize("Apple Inc.", "Apple")
canonical = canonicalizer.canonicalize_entities(text)
canonical = canonicalizer.merge_duplicate_entities(entities)

# Acronym expansion
acronym_expander = AcronymExpander()
expanded = acronym_expander.expand_acronyms("NASA", context="space agency")
expanded = acronym_expander.expand_all_acronyms(text)
acronym_map = acronym_expander.build_acronym_dictionary(text)

# Entity standardization
entity_standardizer = EntityStandardizer()
standardized = entity_standardizer.standardize_entities(text)
standardized = entity_standardizer.normalize_entity_names(entities)
standardized = entity_standardizer.validate_entity_format(entities)
```

**Date Normalizer (`semantica.normalize.date_normalizer`):**
```python
from semantica.normalize.date_normalizer import DateFormatter, DateParser, DateValidator

# Date formatting
date_formatter = DateFormatter()
formatted = date_formatter.format_date("2023-01-15", format="%B %d, %Y")
formatted = date_formatter.normalize_date_format("15/01/2023")
formatted = date_formatter.standardize_dates(text)

# Date parsing
date_parser = DateParser()
parsed = date_parser.parse_date("January 15, 2023")
parsed = date_parser.parse_flexible_date("15th Jan 2023")
parsed = date_parser.extract_dates_from_text(text)

# Date validation
date_validator = DateValidator()
is_valid = date_validator.validate_date("2023-01-15")
is_valid = date_validator.validate_date_range(start_date, end_date)
validation_report = date_validator.validate_dates_in_text(text)
```

**Number Normalizer (`semantica.normalize.number_normalizer`):**
```python
from semantica.normalize.number_normalizer import NumberFormatter, NumberParser, CurrencyNormalizer

# Number formatting
number_formatter = NumberFormatter()
formatted = number_formatter.format_number(1234567.89, format="comma")
formatted = number_formatter.normalize_numbers(text)
formatted = number_formatter.standardize_decimal_separator(text)

# Number parsing
number_parser = NumberParser()
parsed = number_parser.parse_number("1,234,567.89")
parsed = number_parser.parse_flexible_number("1.23M")
parsed = number_parser.extract_numbers_from_text(text)

# Currency normalization
currency_normalizer = CurrencyNormalizer()
normalized = currency_normalizer.normalize_currency("$1,234.56", target_currency="USD")
normalized = currency_normalizer.convert_currency(amount, from_currency="EUR", to_currency="USD")
normalized = currency_normalizer.standardize_currency_format(text)
```

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

**Main Classes:** `SemanticChunker`, `StructuralChunker`, `TableChunker`

**Purpose:** Split documents into optimal chunks for processing

#### **Imports:**
```python
from semantica.split import SemanticChunker, StructuralChunker, TableChunker
from semantica.split.sliding_window import SlidingWindowChunker, OverlapChunker, FixedSizeChunker
from semantica.split.semantic_chunker import TopicBasedChunker, SentenceChunker, ParagraphChunker
from semantica.split.structural_chunker import SectionChunker, HeaderChunker, DocumentChunker
from semantica.split.table_chunker import TablePreservingChunker, TableContextExtractor, TableAwareChunker
from semantica.split.provenance_tracker import ChunkProvenanceTracker, SourceTracker, MetadataTracker
```

#### **Main Functions:**
```python
# Semantic chunking
semantic_chunker = SemanticChunker()
chunks = semantic_chunker.split_by_meaning(long_text)
topics = semantic_chunker.detect_topics(text)
semantic_chunks = semantic_chunker.split_by_semantic_similarity(text)

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

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

#### **Submodules with Functions:**

**Sliding Window (`semantica.split.sliding_window`):**
```python
from semantica.split.sliding_window import SlidingWindowChunker, OverlapChunker, FixedSizeChunker

# Sliding window chunking
sliding_chunker = SlidingWindowChunker(window_size=512, step_size=256)
chunks = sliding_chunker.chunk_text(long_text)
chunks = sliding_chunker.chunk_with_overlap(text, overlap_ratio=0.5)

# Overlap chunking
overlap_chunker = OverlapChunker(chunk_size=500, overlap_size=100)
chunks = overlap_chunker.chunk_text(text)
chunks = overlap_chunker.chunk_with_context(text, context_size=50)

# Fixed size chunking
fixed_chunker = FixedSizeChunker(chunk_size=1000)
chunks = fixed_chunker.chunk_text(text)
chunks = fixed_chunker.chunk_by_tokens(text, max_tokens=500)
```

**Semantic Chunker (`semantica.split.semantic_chunker`):**
```python
from semantica.split.semantic_chunker import TopicBasedChunker, SentenceChunker, ParagraphChunker

# Topic-based chunking
topic_chunker = TopicBasedChunker()
chunks = topic_chunker.split_by_topics(text)
topics = topic_chunker.detect_topic_boundaries(text)
chunks = topic_chunker.split_by_topic_shift(text, threshold=0.7)

# Sentence chunking
sentence_chunker = SentenceChunker()
chunks = sentence_chunker.split_by_sentences(text)
chunks = sentence_chunker.split_by_sentence_similarity(text)
chunks = sentence_chunker.split_by_semantic_coherence(text)

# Paragraph chunking
paragraph_chunker = ParagraphChunker()
chunks = paragraph_chunker.split_by_paragraphs(text)
chunks = paragraph_chunker.split_by_paragraph_similarity(text)
chunks = paragraph_chunker.split_by_paragraph_length(text, max_length=1000)
```

**Structural Chunker (`semantica.split.structural_chunker`):**
```python
from semantica.split.structural_chunker import SectionChunker, HeaderChunker, DocumentChunker

# Section-based chunking
section_chunker = SectionChunker()
chunks = section_chunker.split_by_sections(document)
sections = section_chunker.identify_sections(document)
chunks = section_chunker.split_by_section_hierarchy(document)

# Header-based chunking
header_chunker = HeaderChunker()
chunks = header_chunker.split_by_headers(document)
headers = header_chunker.extract_headers(document)
chunks = header_chunker.split_by_header_level(document, level=2)

# Document-aware chunking
document_chunker = DocumentChunker()
chunks = document_chunker.split_by_document_structure(document)
chunks = document_chunker.split_by_document_type(document, doc_type="research_paper")
chunks = document_chunker.split_by_document_sections(document)
```

**Table Chunker (`semantica.split.table_chunker`):**
```python
from semantica.split.table_chunker import TablePreservingChunker, TableContextExtractor, TableAwareChunker

# Table-preserving chunking
table_preserver = TablePreservingChunker()
chunks = table_preserver.split_preserve_tables(document)
chunks = table_preserver.split_around_tables(document, context_size=200)
chunks = table_preserver.split_table_aware(document)

# Table context extraction
context_extractor = TableContextExtractor()
context = context_extractor.extract_table_context(table)
context = context_extractor.extract_table_metadata(table)
context = context_extractor.extract_table_relationships(table)

# Table-aware chunking
table_aware = TableAwareChunker()
chunks = table_aware.split_with_table_context(document)
chunks = table_aware.split_table_centered(document)
chunks = table_aware.split_table_relationships(document)
```

**Provenance Tracker (`semantica.split.provenance_tracker`):**
```python
from semantica.split.provenance_tracker import ChunkProvenanceTracker, SourceTracker, MetadataTracker

# Chunk provenance tracking
provenance_tracker = ChunkProvenanceTracker()
chunks = provenance_tracker.track_chunk_sources(text, source="document.pdf")
chunks = provenance_tracker.track_chunk_metadata(chunks, metadata={"page": 1, "section": "intro"})
provenance = provenance_tracker.get_chunk_provenance(chunk_id)

# Source tracking
source_tracker = SourceTracker()
sources = source_tracker.track_sources(chunks)
sources = source_tracker.track_source_hierarchy(chunks)
sources = source_tracker.track_source_relationships(chunks)

# Metadata tracking
metadata_tracker = MetadataTracker()
metadata = metadata_tracker.track_metadata(chunks, metadata_schema)
metadata = metadata_tracker.track_metadata_changes(chunks, original_metadata)
metadata = metadata_tracker.track_metadata_provenance(chunks)
```

---

## 🧠 Semantic Intelligence

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

**Main Classes:** `NERExtractor`, `RelationExtractor`, `TripleExtractor`

**Purpose:** Extract semantic information from text

#### **Imports:**
```python
from semantica.semantic_extract import NERExtractor, RelationExtractor, TripleExtractor
from semantica.semantic_extract.ner_extractor import NERModel, EntityClassifier, EntityLinker
from semantica.semantic_extract.relation_extractor import RelationModel, RelationClassifier, RelationValidator
from semantica.semantic_extract.event_detector import EventDetector, TemporalExtractor, EventClassifier
from semantica.semantic_extract.coref_resolver import CorefResolver, EntityLinker, MentionResolver
from semantica.semantic_extract.triple_extractor import TripleModel, TripleValidator, TripleFormatter
from semantica.semantic_extract.llm_enhancer import LLMEnhancer, ComplexExtractor, LLMValidator
```

#### **Main Functions:**
```python
# Named Entity Recognition
ner = NERExtractor()
entities = ner.extract_entities("Apple Inc. was founded by Steve Jobs in 1976")
classified = ner.classify_entities(entities)
linked = ner.link_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)
validated_rels = rel_extractor.validate_relations(relations)

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

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

#### **Submodules with Functions:**

**NER Extractor (`semantica.semantic_extract.ner_extractor`):**
```python
from semantica.semantic_extract.ner_extractor import NERModel, EntityClassifier, EntityLinker

# NER model
ner_model = NERModel(model_name="spacy", language="en")
entities = ner_model.extract_entities(text)
entities = ner_model.extract_entities_batch(texts)
entities = ner_model.extract_entities_with_confidence(text)

# Entity classification
entity_classifier = EntityClassifier()
classified = entity_classifier.classify_entities(entities)
classified = entity_classifier.classify_by_type(entities, entity_type="PERSON")
classified = entity_classifier.classify_by_domain(entities, domain="technology")

# Entity linking
entity_linker = EntityLinker()
linked = entity_linker.link_to_wikidata(entities)
linked = entity_linker.link_to_dbpedia(entities)
linked = entity_linker.link_to_custom_kb(entities, knowledge_base)
```

**Relation Extractor (`semantica.semantic_extract.relation_extractor`):**
```python
from semantica.semantic_extract.relation_extractor import RelationModel, RelationClassifier, RelationValidator

# Relation model
relation_model = RelationModel(model_name="rebel", language="en")
relations = relation_model.extract_relations(text)
relations = relation_model.extract_relations_batch(texts)
relations = relation_model.extract_relations_with_confidence(text)

# Relation classification
relation_classifier = RelationClassifier()
classified = relation_classifier.classify_relations(relations)
classified = relation_classifier.classify_by_type(relations, relation_type="founded_by")
classified = relation_classifier.classify_by_domain(relations, domain="business")

# Relation validation
relation_validator = RelationValidator()
validated = relation_validator.validate_relations(relations)
validated = relation_validator.validate_by_schema(relations, schema)
validated = relation_validator.validate_by_consistency(relations)
```

**Event Detector (`semantica.semantic_extract.event_detector`):**
```python
from semantica.semantic_extract.event_detector import EventDetector, TemporalExtractor, EventClassifier

# Event detection
event_detector = EventDetector()
events = event_detector.detect_events(text)
events = event_detector.detect_events_batch(texts)
events = event_detector.detect_events_with_confidence(text)

# Temporal extraction
temporal_extractor = TemporalExtractor()
temporal = temporal_extractor.extract_temporal_expressions(text)
temporal = temporal_extractor.extract_dates(text)
temporal = temporal_extractor.extract_time_expressions(text)

# Event classification
event_classifier = EventClassifier()
classified = event_classifier.classify_events(events)
classified = event_classifier.classify_by_type(events, event_type="founding")
classified = event_classifier.classify_by_domain(events, domain="business")
```

**Coreference Resolver (`semantica.semantic_extract.coref_resolver`):**
```python
from semantica.semantic_extract.coref_resolver import CorefResolver, EntityLinker, MentionResolver

# Coreference resolution
coref_resolver = CorefResolver()
resolved = coref_resolver.resolve_coreferences(text)
resolved = coref_resolver.resolve_coreferences_batch(texts)
resolved = coref_resolver.resolve_coreferences_with_confidence(text)

# Entity linking
entity_linker = EntityLinker()
linked = entity_linker.link_mentions(mentions)
linked = entity_linker.link_to_knowledge_base(mentions, kb)
linked = entity_linker.link_by_similarity(mentions, threshold=0.8)

# Mention resolution
mention_resolver = MentionResolver()
resolved = mention_resolver.resolve_mentions(mentions)
resolved = mention_resolver.resolve_pronouns(text)
resolved = mention_resolver.resolve_nominal_mentions(text)
```

**Triple Extractor (`semantica.semantic_extract.triple_extractor`):**
```python
from semantica.semantic_extract.triple_extractor import TripleModel, TripleValidator, TripleFormatter

# Triple extraction
triple_model = TripleModel()
triples = triple_model.extract_triples(text)
triples = triple_model.extract_triples_batch(texts)
triples = triple_model.extract_triples_with_confidence(text)

# Triple validation
triple_validator = TripleValidator()
validated = triple_validator.validate_triples(triples)
validated = triple_validator.validate_by_schema(triples, schema)
validated = triple_validator.validate_by_consistency(triples)

# Triple formatting
triple_formatter = TripleFormatter()
formatted = triple_formatter.format_as_rdf(triples)
formatted = triple_formatter.format_as_jsonld(triples)
formatted = triple_formatter.format_as_turtle(triples)
```

**LLM Enhancer (`semantica.semantic_extract.llm_enhancer`):**
```python
from semantica.semantic_extract.llm_enhancer import LLMEnhancer, ComplexExtractor, LLMValidator

# LLM enhancement
llm_enhancer = LLMEnhancer(model="gpt-4")
enhanced = llm_enhancer.enhance_extraction(text, extraction_type="entities")
enhanced = llm_enhancer.enhance_relations(text, relations)
enhanced = llm_enhancer.enhance_triples(text, triples)

# Complex extraction
complex_extractor = ComplexExtractor()
complex_entities = complex_extractor.extract_complex_entities(text)
complex_relations = complex_extractor.extract_complex_relations(text)
complex_events = complex_extractor.extract_complex_events(text)

# LLM validation
llm_validator = LLMValidator()
validated = llm_validator.validate_with_llm(extractions, text)
validated = llm_validator.validate_consistency(extractions)
validated = llm_validator.validate_completeness(extractions, text)
```

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

**Main Class:** `OntologyGenerator`

**Purpose:** Generate ontologies from extracted data

#### **Imports:**
```python
from semantica.ontology import OntologyGenerator
from semantica.ontology.class_inferrer import ClassInferrer, HierarchyBuilder, ClassValidator
from semantica.ontology.property_generator import PropertyInferrer, DataTypeInferrer, PropertyValidator
from semantica.ontology.owl_generator import OWLGenerator, RDFGenerator, TurtleGenerator
from semantica.ontology.base_mapper import SchemaMapper, FOAFMapper, DublinCoreMapper
from semantica.ontology.version_manager import VersionManager, MigrationManager, OntologyVersioner
```

#### **Main Functions:**
```python
# 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)
ontology = ontology_gen.generate_from_entities(entities)
ontology = ontology_gen.generate_from_triples(triples)

# Export in various formats
owl_ontology = ontology.to_owl()
rdf_ontology = ontology.to_rdf()
turtle_ontology = ontology.to_turtle()
jsonld_ontology = ontology.to_jsonld()

# Save to triple store
ontology.save_to_triple_store("http://localhost:9999/blazegraph/sparql")
```

#### **Submodules with Functions:**

**Class Inferrer (`semantica.ontology.class_inferrer`):**
```python
from semantica.ontology.class_inferrer import ClassInferrer, HierarchyBuilder, ClassValidator

# Class inference
class_inferrer = ClassInferrer()
classes = class_inferrer.infer_classes(entities)
classes = class_inferrer.infer_classes_from_triples(triples)
classes = class_inferrer.infer_classes_from_text(text)

# Hierarchy building
hierarchy_builder = HierarchyBuilder()
hierarchy = hierarchy_builder.build_hierarchy(classes)
hierarchy = hierarchy_builder.build_is_a_hierarchy(classes)
hierarchy = hierarchy_builder.build_part_of_hierarchy(classes)

# Class validation
class_validator = ClassValidator()
validated = class_validator.validate_classes(classes)
validated = class_validator.validate_hierarchy(hierarchy)
validated = class_validator.validate_consistency(classes)
```

**Property Generator (`semantica.ontology.property_generator`):**
```python
from semantica.ontology.property_generator import PropertyInferrer, DataTypeInferrer, PropertyValidator

# Property inference
property_inferrer = PropertyInferrer()
properties = property_inferrer.infer_properties(entities)
properties = property_inferrer.infer_properties_from_triples(triples)
properties = property_inferrer.infer_properties_from_text(text)

# Data type inference
datatype_inferrer = DataTypeInferrer()
datatypes = datatype_inferrer.infer_datatypes(properties)
datatypes = datatype_inferrer.infer_datatypes_from_values(values)
datatypes = datatype_inferrer.infer_datatypes_from_schema(schema)

# Property validation
property_validator = PropertyValidator()
validated = property_validator.validate_properties(properties)
validated = property_validator.validate_datatypes(properties, datatypes)
validated = property_validator.validate_domain_range(properties)
```

**OWL Generator (`semantica.ontology.owl_generator`):**
```python
from semantica.ontology.owl_generator import OWLGenerator, RDFGenerator, TurtleGenerator

# OWL generation
owl_generator = OWLGenerator()
owl_ontology = owl_generator.generate_owl(classes, properties)
owl_ontology = owl_generator.generate_owl_from_triples(triples)
owl_ontology = owl_generator.generate_owl_with_axioms(classes, properties, axioms)

# RDF generation
rdf_generator = RDFGenerator()
rdf_ontology = rdf_generator.generate_rdf(classes, properties)
rdf_ontology = rdf_generator.generate_rdf_from_triples(triples)
rdf_ontology = rdf_generator.generate_rdf_with_namespaces(classes, properties, namespaces)

# Turtle generation
turtle_generator = TurtleGenerator()
turtle_ontology = turtle_generator.generate_turtle(classes, properties)
turtle_ontology = turtle_generator.generate_turtle_from_triples(triples)
turtle_ontology = turtle_generator.generate_turtle_with_prefixes(classes, properties, prefixes)
```

**Base Mapper (`semantica.ontology.base_mapper`):**
```python
from semantica.ontology.base_mapper import SchemaMapper, FOAFMapper, DublinCoreMapper

# Schema.org mapping
schema_mapper = SchemaMapper()
mapped = schema_mapper.map_to_schema_org(classes, properties)
mapped = schema_mapper.map_entities_to_schema_org(entities)
mapped = schema_mapper.map_relations_to_schema_org(relations)

# FOAF mapping
foaf_mapper = FOAFMapper()
mapped = foaf_mapper.map_to_foaf(classes, properties)
mapped = foaf_mapper.map_persons_to_foaf(entities)
mapped = foaf_mapper.map_organizations_to_foaf(entities)

# Dublin Core mapping
dc_mapper = DublinCoreMapper()
mapped = dc_mapper.map_to_dublin_core(classes, properties)
mapped = dc_mapper.map_documents_to_dublin_core(documents)
mapped = dc_mapper.map_metadata_to_dublin_core(metadata)
```

**Version Manager (`semantica.ontology.version_manager`):**
```python
from semantica.ontology.version_manager import VersionManager, MigrationManager, OntologyVersioner

# Version management
version_manager = VersionManager()
version = version_manager.create_version(ontology, version="1.0.0")
version = version_manager.get_version(ontology, version="1.0.0")
versions = version_manager.list_versions(ontology)

# Migration management
migration_manager = MigrationManager()
migrated = migration_manager.migrate_ontology(ontology, from_version="1.0.0", to_version="2.0.0")
migrated = migration_manager.migrate_triples(triples, migration_rules)
migrated = migration_manager.migrate_classes(classes, migration_rules)

# Ontology versioning
ontology_versioner = OntologyVersioner()
versioned = ontology_versioner.version_ontology(ontology)
versioned = ontology_versioner.compare_versions(ontology_v1, ontology_v2)
versioned = ontology_versioner.merge_versions(ontology_v1, ontology_v2)
```

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

**Main Classes:** `GraphBuilder`, `EntityResolver`, `Deduplicator`

**Purpose:** Build and manage knowledge graphs

#### **Imports:**
```python
from semantica.kg import GraphBuilder, EntityResolver, Deduplicator
from semantica.kg.graph_builder import NodeBuilder, EdgeBuilder, SubgraphBuilder
from semantica.kg.entity_resolver import IdentityResolver, EntityMerger, EntityMatcher
from semantica.kg.deduplicator import DuplicateFinder, EntityMerger, SimilarityCalculator
from semantica.kg.graph_analyzer import GraphAnalyzer, PathFinder, CentralityCalculator
from semantica.kg.graph_optimizer import GraphOptimizer, IndexBuilder, QueryOptimizer
```

#### **Main Functions:**
```python
# 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)
graph = graph_builder.build_complete_graph(triples)

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

# Deduplication
deduplicator = Deduplicator()
duplicates = deduplicator.find_duplicates(entities)
merged = deduplicator.merge_duplicates(duplicates)
cleaned = deduplicator.clean_graph(graph)
```

#### **Submodules with Functions:**

**Graph Builder (`semantica.kg.graph_builder`):**
```python
from semantica.kg.graph_builder import NodeBuilder, EdgeBuilder, SubgraphBuilder

# Node building
node_builder = NodeBuilder()
node = node_builder.create_node("Apple Inc.", "Company", properties={"founded": 1976})
node = node_builder.create_node_with_id("Apple Inc.", "Company", node_id="apple_inc")
nodes = node_builder.create_nodes_batch(entities)

# Edge building
edge_builder = EdgeBuilder()
edge = edge_builder.create_edge("Apple Inc.", "founded_by", "Steve Jobs")
edge = edge_builder.create_edge_with_properties("Apple Inc.", "founded_by", "Steve Jobs", {"year": 1976})
edges = edge_builder.create_edges_batch(relations)

# Subgraph building
subgraph_builder = SubgraphBuilder()
subgraph = subgraph_builder.build_subgraph(entities, relations)
subgraph = subgraph_builder.build_subgraph_by_type(entities, entity_type="Company")
subgraph = subgraph_builder.build_subgraph_by_relation(entities, relation_type="founded_by")
```

**Entity Resolver (`semantica.kg.entity_resolver`):**
```python
from semantica.kg.entity_resolver import IdentityResolver, EntityMerger, EntityMatcher

# Identity resolution
identity_resolver = IdentityResolver()
canonical = identity_resolver.resolve_identity("Apple Inc.", "Apple")
canonical = identity_resolver.resolve_identity_batch(entities)
canonical = identity_resolver.resolve_identity_with_confidence("Apple Inc.", "Apple", threshold=0.8)

# Entity merging
entity_merger = EntityMerger()
merged = entity_merger.merge_entities(duplicate_entities)
merged = entity_merger.merge_entities_with_strategy(duplicate_entities, strategy="highest_confidence")
merged = entity_merger.merge_entities_with_validation(duplicate_entities, validation_rules)

# Entity matching
entity_matcher = EntityMatcher()
matches = entity_matcher.find_matches("Apple Inc.", entities)
matches = entity_matcher.find_matches_with_similarity("Apple Inc.", entities, threshold=0.8)
matches = entity_matcher.find_matches_with_fuzzy("Apple Inc.", entities, fuzzy_threshold=0.7)
```

**Deduplicator (`semantica.kg.deduplicator`):**
```python
from semantica.kg.deduplicator import DuplicateFinder, EntityMerger, SimilarityCalculator

# Duplicate finding
duplicate_finder = DuplicateFinder()
duplicates = duplicate_finder.find_duplicates(entities)
duplicates = duplicate_finder.find_duplicates_by_similarity(entities, threshold=0.8)
duplicates = duplicate_finder.find_duplicates_by_fuzzy_matching(entities, fuzzy_threshold=0.7)

# Entity merging
entity_merger = EntityMerger()
merged = entity_merger.merge_duplicates(duplicates)
merged = entity_merger.merge_duplicates_with_strategy(duplicates, strategy="merge_properties")
merged = entity_merger.merge_duplicates_with_validation(duplicates, validation_rules)

# Similarity calculation
similarity_calculator = SimilarityCalculator()
similarity = similarity_calculator.calculate_similarity("Apple Inc.", "Apple")
similarity = similarity_calculator.calculate_similarity_batch(entities)
similarity = similarity_calculator.calculate_similarity_with_weights(entities, weights)
```

**Graph Analyzer (`semantica.kg.graph_analyzer`):**
```python
from semantica.kg.graph_analyzer import GraphAnalyzer, PathFinder, CentralityCalculator

# Graph analysis
graph_analyzer = GraphAnalyzer()
stats = graph_analyzer.analyze_graph(graph)
stats = graph_analyzer.analyze_connectivity(graph)
stats = graph_analyzer.analyze_centrality(graph)

# Path finding
path_finder = PathFinder()
paths = path_finder.find_paths("Apple Inc.", "Steve Jobs", graph)
paths = path_finder.find_shortest_paths("Apple Inc.", "Steve Jobs", graph)
paths = path_finder.find_all_paths("Apple Inc.", "Steve Jobs", graph, max_length=3)

# Centrality calculation
centrality_calculator = CentralityCalculator()
centrality = centrality_calculator.calculate_centrality(graph)
centrality = centrality_calculator.calculate_betweenness_centrality(graph)
centrality = centrality_calculator.calculate_eigenvector_centrality(graph)
```

**Graph Optimizer (`semantica.kg.graph_optimizer`):**
```python
from semantica.kg.graph_optimizer import GraphOptimizer, IndexBuilder, QueryOptimizer

# Graph optimization
graph_optimizer = GraphOptimizer()
optimized = graph_optimizer.optimize_graph(graph)
optimized = graph_optimizer.optimize_for_queries(graph, query_patterns)
optimized = graph_optimizer.optimize_for_storage(graph)

# Index building
index_builder = IndexBuilder()
index = index_builder.build_index(graph)
index = index_builder.build_property_index(graph, properties=["name", "type"])
index = index_builder.build_relationship_index(graph, relationships=["founded_by"])

# Query optimization
query_optimizer = QueryOptimizer()
optimized_query = query_optimizer.optimize_query(query, graph)
optimized_query = query_optimizer.optimize_query_with_index(query, graph, index)
optimized_query = query_optimizer.optimize_query_with_statistics(query, graph, statistics)
```

**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

#### **Imports:**
```python
from semantica.vector_store import PineconeAdapter, FAISSAdapter, WeaviateAdapter
from semantica.vector_store.pinecone_adapter import PineconeIndex, PineconeQuery, PineconeMetadata
from semantica.vector_store.faiss_adapter import FAISSIndex, FAISSSearch, FAISSIndexBuilder
from semantica.vector_store.milvus_adapter import MilvusClient, MilvusCollection, MilvusSearch
from semantica.vector_store.weaviate_adapter import WeaviateClient, WeaviateSchema, WeaviateQuery
from semantica.vector_store.qdrant_adapter import QdrantClient, QdrantCollection, QdrantSearch
from semantica.vector_store.hybrid_search import HybridSearch, MetadataFilter, SearchRanker
```

#### **Main Functions:**
```python
# 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)

# Weaviate integration
weaviate = WeaviateAdapter()
weaviate.connect(url="http://localhost:8080")
weaviate.create_schema(schema_definition)
weaviate.insert_objects(objects)
results = weaviate.query_objects(query, class_name="Document")
```

#### **Submodules with Functions:**

**Pinecone Adapter (`semantica.vector_store.pinecone_adapter`):**
```python
from semantica.vector_store.pinecone_adapter import PineconeIndex, PineconeQuery, PineconeMetadata

# Index management
pinecone_index = PineconeIndex()
pinecone_index.create_index("semantica-index", dimension=1536, metric="cosine")
pinecone_index.describe_index("semantica-index")
pinecone_index.delete_index("semantica-index")

# Vector operations
pinecone_index.upsert_vectors(vectors, metadata, namespace="documents")
pinecone_index.update_vectors(vectors, metadata)
pinecone_index.delete_vectors(ids)

# Query operations
pinecone_query = PineconeQuery()
results = pinecone_query.query_vectors(query_vector, top_k=10, include_metadata=True)
results = pinecone_query.query_with_filter(query_vector, filter={"category": "technology"})
results = pinecone_query.query_by_id(vector_id)

# Metadata operations
pinecone_metadata = PineconeMetadata()
metadata = pinecone_metadata.create_metadata({"text": "sample", "category": "tech"})
metadata = pinecone_metadata.validate_metadata(metadata)
metadata = pinecone_metadata.filter_metadata(metadata, {"category": "technology"})
```

**FAISS Adapter (`semantica.vector_store.faiss_adapter`):**
```python
from semantica.vector_store.faiss_adapter import FAISSIndex, FAISSSearch, FAISSIndexBuilder

# Index building
index_builder = FAISSIndexBuilder()
index = index_builder.build_index("IVFFlat", dimension=1536, nlist=100)
index = index_builder.build_index("HNSW", dimension=1536, M=16)
index = index_builder.build_index("PQ", dimension=1536, m=64)

# Index operations
faiss_index = FAISSIndex()
faiss_index.add_vectors(vectors)
faiss_index.add_vectors_with_ids(vectors, ids)
faiss_index.remove_vectors(ids)
faiss_index.save_index("index.faiss")
faiss_index.load_index("index.faiss")

# Search operations
faiss_search = FAISSSearch()
results = faiss_search.search_similar(query_vector, k=10)
results = faiss_search.search_with_ids(query_vector, ids, k=10)
results = faiss_search.search_range(query_vector, radius=0.5)
```

**Weaviate Adapter (`semantica.vector_store.weaviate_adapter`):**
```python
from semantica.vector_store.weaviate_adapter import WeaviateClient, WeaviateSchema, WeaviateQuery

# Client operations
weaviate_client = WeaviateClient()
weaviate_client.connect(url="http://localhost:8080")
weaviate_client.get_cluster_info()
weaviate_client.get_schema()

# Schema operations
weaviate_schema = WeaviateSchema()
weaviate_schema.create_class(class_definition)
weaviate_schema.update_class(class_name, class_definition)
weaviate_schema.delete_class(class_name)

# Query operations
weaviate_query = WeaviateQuery()
results = weaviate_query.query_objects(query, class_name="Document")
results = weaviate_query.query_with_filters(query, filters={"category": "technology"})
results = weaviate_query.query_similar(query_vector, class_name="Document", limit=10)
```

**Hybrid Search (`semantica.vector_store.hybrid_search`):**
```python
from semantica.vector_store.hybrid_search import HybridSearch, MetadataFilter, SearchRanker

# Hybrid search
hybrid_search = HybridSearch()
results = hybrid_search.search(query, vector_weight=0.7, metadata_weight=0.3)
results = hybrid_search.search_with_filters(query, filters={"category": "technology"})
results = hybrid_search.search_with_reranking(query, rerank_method="diversity")

# Metadata filtering
metadata_filter = MetadataFilter()
filtered = metadata_filter.filter_by_metadata(results, {"category": "technology"})
filtered = metadata_filter.filter_by_range(results, "date", start="2023-01-01", end="2023-12-31")
filtered = metadata_filter.filter_by_boolean(results, {"is_published": True})

# Search ranking
search_ranker = SearchRanker()
ranked = search_ranker.rank_by_relevance(results, query)
ranked = search_ranker.rank_by_diversity(results, diversity_threshold=0.8)
ranked = search_ranker.rank_by_hybrid_score(results, vector_score=0.6, metadata_score=0.4)
```

### 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

#### **Imports:**
```python
from semantica.qa_rag import RAGManager, SemanticChunker, AnswerBuilder
from semantica.qa_rag.semantic_chunker import RAGChunker, ContextChunker, OverlapChunker
from semantica.qa_rag.prompt_templates import PromptTemplate, ContextTemplate, AnswerTemplate
from semantica.qa_rag.retrieval_policies import RetrievalPolicy, RankingPolicy, FilterPolicy
from semantica.qa_rag.answer_builder import AnswerBuilder, AttributionBuilder, ConfidenceCalculator
from semantica.qa_rag.provenance_tracker import SourceTracker, ConfidenceTracker, AttributionTracker
from semantica.qa_rag.conversation_manager import ConversationManager, ContextManager, HistoryManager
```

#### **Main Functions:**
```python
# 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)
chunks = chunker.chunk_with_context(document, context_window=200)
```

#### **Submodules with Functions:**

**Semantic Chunker (`semantica.qa_rag.semantic_chunker`):**
```python
from semantica.qa_rag.semantic_chunker import RAGChunker, ContextChunker, OverlapChunker

# RAG-optimized chunking
rag_chunker = RAGChunker()
chunks = rag_chunker.chunk_for_rag(document, chunk_size=512)
chunks = rag_chunker.chunk_with_semantic_boundaries(document)
chunks = rag_chunker.chunk_with_entity_preservation(document)

# Context-aware chunking
context_chunker = ContextChunker()
chunks = context_chunker.chunk_with_context(document, context_size=100)
chunks = context_chunker.chunk_with_overlap_context(document, overlap=50)
chunks = context_chunker.chunk_with_semantic_context(document)

# Overlap chunking
overlap_chunker = OverlapChunker()
chunks = overlap_chunker.chunk_with_overlap(document, overlap_ratio=0.2)
chunks = overlap_chunker.chunk_with_sliding_window(document, window_size=512, step=256)
chunks = overlap_chunker.chunk_with_adaptive_overlap(document)
```

**Prompt Templates (`semantica.qa_rag.prompt_templates`):**
```python
from semantica.qa_rag.prompt_templates import PromptTemplate, ContextTemplate, AnswerTemplate

# Prompt template management
prompt_template = PromptTemplate()
template = prompt_template.create_template("qa_template", question="{question}", context="{context}")
template = prompt_template.create_template("summarization", text="{text}", length="{length}")
template = prompt_template.create_template("classification", text="{text}", categories="{categories}")

# Context templates
context_template = ContextTemplate()
context = context_template.format_context(retrieved_chunks, question)
context = context_template.format_context_with_metadata(retrieved_chunks, question, metadata)
context = context_template.format_context_with_ranking(retrieved_chunks, question, rankings)

# Answer templates
answer_template = AnswerTemplate()
answer = answer_template.format_answer(response, sources)
answer = answer_template.format_answer_with_attribution(response, sources, attributions)
answer = answer_template.format_answer_with_confidence(response, sources, confidence_scores)
```

**Retrieval Policies (`semantica.qa_rag.retrieval_policies`):**
```python
from semantica.qa_rag.retrieval_policies import RetrievalPolicy, RankingPolicy, FilterPolicy

# Retrieval policy management
retrieval_policy = RetrievalPolicy()
results = retrieval_policy.retrieve(query, top_k=10)
results = retrieval_policy.retrieve_with_filters(query, filters={"category": "technology"})
results = retrieval_policy.retrieve_with_reranking(query, rerank_method="diversity")

# Ranking policies
ranking_policy = RankingPolicy()
ranked = ranking_policy.rank_by_relevance(results, query)
ranked = ranking_policy.rank_by_diversity(results, diversity_threshold=0.8)
ranked = ranking_policy.rank_by_hybrid_score(results, vector_weight=0.7, metadata_weight=0.3)

# Filter policies
filter_policy = FilterPolicy()
filtered = filter_policy.filter_by_metadata(results, {"category": "technology"})
filtered = filter_policy.filter_by_date_range(results, start_date="2023-01-01", end_date="2023-12-31")
filtered = filter_policy.filter_by_confidence(results, min_confidence=0.8)
```

**Answer Builder (`semantica.qa_rag.answer_builder`):**
```python
from semantica.qa_rag.answer_builder import AnswerBuilder, AttributionBuilder, ConfidenceCalculator

# Answer construction
answer_builder = AnswerBuilder()
answer = answer_builder.build_answer(query, retrieved_chunks, llm_response)
answer = answer_builder.build_answer_with_sources(query, retrieved_chunks, llm_response, sources)
answer = answer_builder.build_answer_with_confidence(query, retrieved_chunks, llm_response, confidence)

# Attribution building
attribution_builder = AttributionBuilder()
attributions = attribution_builder.build_attributions(answer, sources)
attributions = attribution_builder.build_attributions_with_confidence(answer, sources, confidence_scores)
attributions = attribution_builder.build_attributions_with_metadata(answer, sources, metadata)

# Confidence calculation
confidence_calculator = ConfidenceCalculator()
confidence = confidence_calculator.calculate_confidence(answer, sources)
confidence = confidence_calculator.calculate_confidence_with_llm(answer, sources, llm_confidence)
confidence = confidence_calculator.calculate_confidence_with_retrieval(answer, sources, retrieval_scores)
```

**Conversation Manager (`semantica.qa_rag.conversation_manager`):**
```python
from semantica.qa_rag.conversation_manager import ConversationManager, ContextManager, HistoryManager

# Conversation management
conversation_manager = ConversationManager()
conversation = conversation_manager.start_conversation()
conversation = conversation_manager.add_turn(conversation, question, answer)
conversation = conversation_manager.get_conversation_history(conversation_id)

# Context management
context_manager = ContextManager()
context = context_manager.build_context(conversation_history)
context = context_manager.build_context_with_entities(conversation_history, entities)
context = context_manager.build_context_with_topics(conversation_history, topics)

# History management
history_manager = HistoryManager()
history = history_manager.save_conversation(conversation)
history = history_manager.load_conversation(conversation_id)
history = history_manager.search_conversations(query, filters={"user_id": "user123"})
```

### 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

---

## 🚀 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?")
```


---

## 📚 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)

---

## 🔧 Knowledge Graph Quality Assurance

> **Addressing the fundamental challenges in building production-ready Knowledge Graphs**

### 16. **Template System** (`semantica.templates`)

**Main Classes:** `SchemaTemplate`, `EntityTemplate`, `RelationshipTemplate`

**Purpose:** Enforce fixed, predefined schemas for consistent and predictable Knowledge Graph structure

#### **Imports:**
```python
from semantica.templates import SchemaTemplate, EntityTemplate, RelationshipTemplate
from semantica.templates.schema_manager import SchemaManager, SchemaValidator, SchemaEnforcer
from semantica.templates.entity_templates import EntityTemplateManager, EntityValidator, EntityEnforcer
from semantica.templates.relationship_templates import RelationshipTemplateManager, RelationshipValidator
from semantica.templates.template_loader import TemplateLoader, TemplateParser, TemplateCompiler
from semantica.templates.constraint_enforcer import ConstraintEnforcer, ValidationEngine, ComplianceChecker
```

#### **Main Functions:**
```python
# Define fixed schema template
schema_template = SchemaTemplate(
    name="business_knowledge_graph",
    entities=["Company", "Person", "Product", "Department", "Project"],
    relationships=["founded_by", "works_for", "manages", "belongs_to", "reports_to"],
    constraints={
        "Company": {"required_props": ["name", "industry", "founded_year"]},
        "Person": {"required_props": ["name", "title", "department"]},
        "founded_by": {"domain": "Company", "range": "Person"}
    }
)

# Enforce template compliance
schema_template.enforce_schema(extracted_entities)
schema_template.validate_relationships(extracted_relations)
schema_template.apply_constraints(knowledge_graph)
```

#### **Submodules with Functions:**

**Schema Manager (`semantica.templates.schema_manager`):**
```python
from semantica.templates.schema_manager import SchemaManager, SchemaValidator, SchemaEnforcer

# Schema management
schema_manager = SchemaManager()
schema = schema_manager.load_schema("business_schema.yaml")
schema = schema_manager.create_schema_from_template(template)
schema = schema_manager.update_schema(schema, updates)

# Schema validation
schema_validator = SchemaValidator()
is_valid = schema_validator.validate_entities(entities, schema)
is_valid = schema_validator.validate_relationships(relationships, schema)
is_valid = schema_validator.validate_properties(properties, schema)

# Schema enforcement
schema_enforcer = SchemaEnforcer()
enforced = schema_enforcer.enforce_entity_schema(entities, schema)
enforced = schema_enforcer.enforce_relationship_schema(relationships, schema)
enforced = schema_enforcer.enforce_property_schema(properties, schema)
```

**Entity Templates (`semantica.templates.entity_templates`):**
```python
from semantica.templates.entity_templates import EntityTemplateManager, EntityValidator, EntityEnforcer

# Entity template management
entity_template_manager = EntityTemplateManager()
template = entity_template_manager.create_template("Company", required_props=["name", "industry"])
template = entity_template_manager.load_template("person_template.yaml")
template = entity_template_manager.update_template(template, new_constraints)

# Entity validation
entity_validator = EntityValidator()
is_valid = entity_validator.validate_entity(entity, template)
is_valid = entity_validator.validate_entity_batch(entities, template)
is_valid = entity_validator.validate_required_properties(entity, template)

# Entity enforcement
entity_enforcer = EntityEnforcer()
enforced = entity_enforcer.enforce_template(entity, template)
enforced = entity_enforcer.add_missing_properties(entity, template)
enforced = entity_enforcer.normalize_entity(entity, template)
```

### 17. **Seed Data System** (`semantica.seed`)

**Main Classes:** `SeedDataManager`, `KnowledgeSeeder`, `DataIntegrator`

**Purpose:** Initialize Knowledge Graph with pre-existing, verified data to build on foundation of truth

#### **Imports:**
```python
from semantica.seed import SeedDataManager, KnowledgeSeeder, DataIntegrator
from semantica.seed.data_loader import CSVLoader, JSONLoader, DatabaseLoader, APILoader
from semantica.seed.entity_seeder import EntitySeeder, RelationshipSeeder, PropertySeeder
from semantica.seed.verification import DataVerifier, ConsistencyChecker, TruthValidator
from semantica.seed.integration import DataIntegrator, ConflictResolver, MergeStrategy
```

#### **Main Functions:**
```python
# Initialize with seed data
seed_manager = SeedDataManager()
seed_manager.load_products("products.csv")
seed_manager.load_departments("departments.json")
seed_manager.load_employees("employees_db")

# Seed knowledge graph
knowledge_seeder = KnowledgeSeeder()
seeded_graph = knowledge_seeder.seed_entities(seed_data)
seeded_graph = knowledge_seeder.seed_relationships(seed_data)
seeded_graph = knowledge_seeder.seed_properties(seed_data)

# Integrate with extracted data
integrator = DataIntegrator()
integrated = integrator.integrate_seed_with_extracted(seed_data, extracted_data)
integrated = integrator.merge_verified_data(seed_data, extracted_data)
```

#### **Submodules with Functions:**

**Data Loader (`semantica.seed.data_loader`):**
```python
from semantica.seed.data_loader import CSVLoader, JSONLoader, DatabaseLoader, APILoader

# CSV data loading
csv_loader = CSVLoader()
products = csv_loader.load_entities("products.csv", entity_type="Product")
departments = csv_loader.load_entities("departments.csv", entity_type="Department")

# JSON data loading
json_loader = JSONLoader()
employees = json_loader.load_entities("employees.json", entity_type="Person")
companies = json_loader.load_entities("companies.json", entity_type="Company")

# Database loading
db_loader = DatabaseLoader(connection_string="postgresql://...")
customers = db_loader.load_entities("customers", entity_type="Person")
orders = db_loader.load_relationships("orders", relationship_type="placed_by")

# API loading
api_loader = APILoader(api_key="your_key")
external_data = api_loader.load_from_api("https://api.example.com/entities")
```

**Entity Seeder (`semantica.seed.entity_seeder`):**
```python
from semantica.seed.entity_seeder import EntitySeeder, RelationshipSeeder, PropertySeeder

# Entity seeding
entity_seeder = EntitySeeder()
seeded = entity_seeder.seed_entities(products, entity_type="Product")
seeded = entity_seeder.seed_with_verification(employees, verification_rules)
seeded = entity_seeder.seed_with_metadata(companies, metadata={"source": "verified"})

# Relationship seeding
relationship_seeder = RelationshipSeeder()
seeded = relationship_seeder.seed_relationships(org_chart, relationship_type="reports_to")
seeded = relationship_seeder.seed_with_hierarchy(relationships, hierarchy_rules)

# Property seeding
property_seeder = PropertySeeder()
seeded = property_seeder.seed_properties(entities, property_mappings)
seeded = property_seeder.seed_with_validation(properties, validation_rules)
```

### 18. **Advanced Deduplication** (`semantica.deduplication`)

**Main Classes:** `DuplicateDetector`, `EntityMerger`, `SimilarityEngine`

**Purpose:** Identify and merge duplicate entities like "First Quarter Sales" and "Q1 Sales Report"

#### **Imports:**
```python
from semantica.deduplication import DuplicateDetector, EntityMerger, SimilarityEngine
from semantica.deduplication.similarity_calculator import SemanticSimilarity, FuzzyMatcher, PhoneticMatcher
from semantica.deduplication.merge_strategies import MergeStrategy, PropertyMerger, RelationshipMerger
from semantica.deduplication.conflict_resolver import ConflictResolver, MergeConflictHandler, ResolutionStrategy
from semantica.deduplication.quality_assessor import QualityAssessor, MergeQualityChecker, ConfidenceCalculator
```

#### **Main Functions:**
```python
# Detect duplicates
duplicate_detector = DuplicateDetector()
duplicates = duplicate_detector.find_semantic_duplicates(entities)
duplicates = duplicate_detector.find_fuzzy_duplicates(entities, threshold=0.8)
duplicates = duplicate_detector.find_phonetic_duplicates(entities)

# Merge duplicates
entity_merger = EntityMerger()
merged = entity_merger.merge_duplicates(duplicates)
merged = entity_merger.merge_with_strategy(duplicates, strategy="highest_confidence")
merged = entity_merger.merge_with_validation(duplicates, validation_rules)

# Calculate similarity
similarity_engine = SimilarityEngine()
similarity = similarity_engine.calculate_semantic_similarity("First Quarter Sales", "Q1 Sales Report")
similarity = similarity_engine.calculate_fuzzy_similarity("Apple Inc.", "Apple Corporation")
similarity = similarity_engine.calculate_phonetic_similarity("Smith", "Smyth")
```

#### **Submodules with Functions:**

**Similarity Calculator (`semantica.deduplication.similarity_calculator`):**
```python
from semantica.deduplication.similarity_calculator import SemanticSimilarity, FuzzyMatcher, PhoneticMatcher

# Semantic similarity
semantic_similarity = SemanticSimilarity()
score = semantic_similarity.calculate("First Quarter Sales", "Q1 Sales Report")
score = semantic_similarity.calculate_batch(entity_pairs)
score = semantic_similarity.calculate_with_embeddings(entity1, entity2)

# Fuzzy matching
fuzzy_matcher = FuzzyMatcher()
matches = fuzzy_matcher.find_matches("Apple Inc.", entities, threshold=0.8)
matches = fuzzy_matcher.find_partial_matches("Apple", entities, threshold=0.6)
matches = fuzzy_matcher.find_approximate_matches("Q1", entities, threshold=0.7)

# Phonetic matching
phonetic_matcher = PhoneticMatcher()
matches = phonetic_matcher.find_phonetic_matches("Smith", entities)
matches = phonetic_matcher.find_soundex_matches("Johnson", entities)
matches = phonetic_matcher.find_metaphone_matches("Knight", entities)
```

**Merge Strategies (`semantica.deduplication.merge_strategies`):**
```python
from semantica.deduplication.merge_strategies import MergeStrategy, PropertyMerger, RelationshipMerger

# Merge strategy management
merge_strategy = MergeStrategy()
merged = merge_strategy.merge_by_confidence(duplicates)
merged = merge_strategy.merge_by_completeness(duplicates)
merged = merge_strategy.merge_by_authority(duplicates, authority_sources)

# Property merging
property_merger = PropertyMerger()
merged = property_merger.merge_properties(duplicate_entities)
merged = property_merger.merge_with_priority(duplicate_entities, priority_rules)
merged = property_merger.merge_with_validation(duplicate_entities, validation_rules)

# Relationship merging
relationship_merger = RelationshipMerger()
merged = relationship_merger.merge_relationships(duplicate_entities)
merged = relationship_merger.merge_with_deduplication(duplicate_entities)
merged = relationship_merger.merge_with_consistency_check(duplicate_entities)
```

### 19. **Conflict Detection & Source Tracking** (`semantica.conflicts`)

**Main Classes:** `ConflictDetector`, `SourceTracker`, `DisagreementResolver`

**Purpose:** Flag when sources disagree and track exact document origins for investigation

#### **Imports:**
```python
from semantica.conflicts import ConflictDetector, SourceTracker, DisagreementResolver
from semantica.conflicts.conflict_analyzer import ConflictAnalyzer, DisagreementDetector, InconsistencyFinder
from semantica.conflicts.source_tracker import SourceTracker, DocumentTracker, ProvenanceTracker
from semantica.conflicts.resolution_strategies import ResolutionStrategy, VotingResolver, AuthorityResolver
from semantica.conflicts.reporting import ConflictReporter, DisagreementReporter, InvestigationGuide
```

#### **Main Functions:**
```python
# Detect conflicts
conflict_detector = ConflictDetector()
conflicts = conflict_detector.detect_value_conflicts(entities, "sales_figure")
conflicts = conflict_detector.detect_property_conflicts(entities, "founded_year")
conflicts = conflict_detector.detect_relationship_conflicts(relationships)

# Track sources
source_tracker = SourceTracker()
sources = source_tracker.track_entity_sources(entity, "Apple Inc.")
sources = source_tracker.track_property_sources(property, "sales_figure", "$10M")
sources = source_tracker.track_relationship_sources(relationship, "founded_by")

# Resolve disagreements
disagreement_resolver = DisagreementResolver()
resolved = disagreement_resolver.resolve_by_voting(conflicts)
resolved = disagreement_resolver.resolve_by_authority(conflicts, authority_sources)
resolved = disagreement_resolver.flag_for_investigation(conflicts)
```

#### **Submodules with Functions:**

**Conflict Analyzer (`semantica.conflicts.conflict_analyzer`):**
```python
from semantica.conflicts.conflict_analyzer import ConflictAnalyzer, DisagreementDetector, InconsistencyFinder

# Conflict analysis
conflict_analyzer = ConflictAnalyzer()
conflicts = conflict_analyzer.analyze_value_conflicts(entities, property_name="sales_figure")
conflicts = conflict_analyzer.analyze_type_conflicts(entities, property_name="founded_year")
conflicts = conflict_analyzer.analyze_relationship_conflicts(relationships)

# Disagreement detection
disagreement_detector = DisagreementDetector()
disagreements = disagreement_detector.detect_value_disagreements(entities, "sales_figure")
disagreements = disagreement_detector.detect_factual_disagreements(entities, "founded_year")
disagreements = disagreement_detector.detect_categorical_disagreements(entities, "industry")

# Inconsistency finding
inconsistency_finder = InconsistencyFinder()
inconsistencies = inconsistency_finder.find_logical_inconsistencies(knowledge_graph)
inconsistencies = inconsistency_finder.find_temporal_inconsistencies(entities, "founded_year")
inconsistencies = inconsistency_finder.find_hierarchical_inconsistencies(relationships)
```

**Source Tracker (`semantica.conflicts.source_tracker`):**
```python
from semantica.conflicts.source_tracker import SourceTracker, DocumentTracker, ProvenanceTracker

# Source tracking
source_tracker = SourceTracker()
sources = source_tracker.track_entity_sources(entity, "Apple Inc.")
sources = source_tracker.track_property_sources(property, "sales_figure", "$10M")
sources = source_tracker.track_relationship_sources(relationship, "founded_by")

# Document tracking
document_tracker = DocumentTracker()
documents = document_tracker.get_source_documents(entity, "Apple Inc.")
documents = document_tracker.get_document_sections(entity, "Apple Inc.", "sales_figure")
documents = document_tracker.get_document_context(entity, "Apple Inc.", context_size=200)

# Provenance tracking
provenance_tracker = ProvenanceTracker()
provenance = provenance_tracker.get_entity_provenance(entity, "Apple Inc.")
provenance = provenance_tracker.get_property_provenance(property, "sales_figure")
provenance = provenance_tracker.get_relationship_provenance(relationship, "founded_by")
```

**Conflict Reporter (`semantica.conflicts.reporting`):**
```python
from semantica.conflicts.reporting import ConflictReporter, DisagreementReporter, InvestigationGuide

# Conflict reporting
conflict_reporter = ConflictReporter()
report = conflict_reporter.generate_conflict_report(conflicts)
report = conflict_reporter.generate_detailed_report(conflicts, include_sources=True)
report = conflict_reporter.generate_summary_report(conflicts)

# Disagreement reporting
disagreement_reporter = DisagreementReporter()
report = disagreement_reporter.report_value_disagreements(disagreements, "sales_figure")
report = disagreement_reporter.report_factual_disagreements(disagreements, "founded_year")
report = disagreement_reporter.report_categorical_disagreements(disagreements, "industry")

# Investigation guide
investigation_guide = InvestigationGuide()
guide = investigation_guide.create_investigation_plan(conflicts)
guide = investigation_guide.suggest_investigation_steps(conflicts)
guide = investigation_guide.prioritize_investigations(conflicts, priority_criteria)
```

### 20. **Knowledge Graph Quality Assurance** (`semantica.kg_qa`)

**Main Classes:** `KGQualityAssessor`, `ConsistencyChecker`, `CompletenessValidator`

**Purpose:** Comprehensive quality assurance for production-ready Knowledge Graphs

#### **Imports:**
```python
from semantica.kg_qa import KGQualityAssessor, ConsistencyChecker, CompletenessValidator
from semantica.kg_qa.quality_metrics import QualityMetrics, CompletenessMetrics, ConsistencyMetrics
from semantica.kg_qa.validation_engine import ValidationEngine, RuleValidator, ConstraintValidator
from semantica.kg_qa.reporting import QualityReporter, IssueTracker, ImprovementSuggestions
from semantica.kg_qa.automated_fixes import AutomatedFixer, AutoMerger, AutoResolver
```

#### **Main Functions:**
```python
# Assess knowledge graph quality
kg_qa = KGQualityAssessor()
quality_score = kg_qa.assess_overall_quality(knowledge_graph)
quality_report = kg_qa.generate_quality_report(knowledge_graph)
quality_issues = kg_qa.identify_quality_issues(knowledge_graph)

# Check consistency
consistency_checker = ConsistencyChecker()
is_consistent = consistency_checker.check_logical_consistency(knowledge_graph)
is_consistent = consistency_checker.check_temporal_consistency(knowledge_graph)
is_consistent = consistency_checker.check_hierarchical_consistency(knowledge_graph)

# Validate completeness
completeness_validator = CompletenessValidator()
is_complete = completeness_validator.validate_entity_completeness(entities, schema)
is_complete = completeness_validator.validate_relationship_completeness(relationships, schema)
is_complete = completeness_validator.validate_property_completeness(properties, schema)
```

#### **Submodules with Functions:**

**Quality Metrics (`semantica.kg_qa.quality_metrics`):**
```python
from semantica.kg_qa.quality_metrics import QualityMetrics, CompletenessMetrics, ConsistencyMetrics

# Quality metrics calculation
quality_metrics = QualityMetrics()
score = quality_metrics.calculate_overall_score(knowledge_graph)
score = quality_metrics.calculate_entity_quality(entities)
score = quality_metrics.calculate_relationship_quality(relationships)

# Completeness metrics
completeness_metrics = CompletenessMetrics()
score = completeness_metrics.calculate_entity_completeness(entities, schema)
score = completeness_metrics.calculate_property_completeness(properties, schema)
score = completeness_metrics.calculate_relationship_completeness(relationships, schema)

# Consistency metrics
consistency_metrics = ConsistencyMetrics()
score = consistency_metrics.calculate_logical_consistency(knowledge_graph)
score = consistency_metrics.calculate_temporal_consistency(knowledge_graph)
score = consistency_metrics.calculate_hierarchical_consistency(knowledge_graph)
```

**Automated Fixes (`semantica.kg_qa.automated_fixes`):**
```python
from semantica.kg_qa.automated_fixes import AutomatedFixer, AutoMerger, AutoResolver

# Automated fixing
automated_fixer = AutomatedFixer()
fixed = automated_fixer.fix_duplicates(knowledge_graph)
fixed = automated_fixer.fix_inconsistencies(knowledge_graph)
fixed = automated_fixer.fix_missing_properties(knowledge_graph)

# Auto merging
auto_merger = AutoMerger()
merged = auto_merger.merge_duplicate_entities(knowledge_graph)
merged = auto_merger.merge_duplicate_relationships(knowledge_graph)
merged = auto_merger.merge_conflicting_properties(knowledge_graph)

# Auto resolving
auto_resolver = AutoResolver()
resolved = auto_resolver.resolve_conflicts(knowledge_graph)
resolved = auto_resolver.resolve_disagreements(knowledge_graph)
resolved = auto_resolver.resolve_inconsistencies(knowledge_graph)
```

---

## 📚 Complete Module Index

### All 20 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 | **Template System** | `semantica.templates` | `SchemaTemplate`, `EntityTemplate` | 5 |
| 17 | **Seed Data System** | `semantica.seed` | `SeedDataManager`, `KnowledgeSeeder` | 4 |
| 18 | **Advanced Deduplication** | `semantica.deduplication` | `DuplicateDetector`, `EntityMerger` | 5 |
| 19 | **Conflict Detection** | `semantica.conflicts` | `ConflictDetector`, `SourceTracker` | 4 |
| 20 | **KG Quality Assurance** | `semantica.kg_qa` | `KGQualityAssessor`, `ConsistencyChecker` | 5 |

**Total: 20 Main Modules, 120+ 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 |

| `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 |

**Total: 20 Modules, 400+ 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
)


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

# Additional utilities
from semantica.utils import (
    DataValidator, SchemaManager, TemplateManager, SeedManager,
    SemanticDeduplicator, ConflictDetector, MultiProviderConfig, 
    AnalyticsDashboard, BusinessIntelligenceDashboard,
    HealthcareProcessor, 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.pipelines import ResearchPipeline, BusinessIntelligenceDashboard
from semantica.healthcare import HealthcareProcessor
from semantica.deployment import EnterpriseDeployment
from semantica.analytics import AnalyticsDashboard

# Advanced usage
from semantica.config import MultiProviderConfig
```

---

## 🔧 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**: 20
- **Total Submodules**: 120+
- **Total Classes**: 200+
- **Total Functions**: 1000+
- **Supported Formats**: 50+
- **Supported Languages**: 100+
- **Integration Points**: 30+

---

## 🎯 Solving Real-World Knowledge Graph Challenges

> **Complete solution for the fundamental problems in building production-ready Knowledge Graphs**

### **Problem 1: Stick to a Fixed Template**
```python
from semantica.templates import SchemaTemplate, SchemaEnforcer

# Define your business-specific schema
business_schema = SchemaTemplate(
    name="company_knowledge_graph",
    entities=["Company", "Person", "Product", "Department", "Project", "Quarterly_Report"],
    relationships=["founded_by", "works_for", "manages", "belongs_to", "reports_to", "produces"],
    constraints={
        "Company": {"required_props": ["name", "industry", "founded_year", "headquarters"]},
        "Person": {"required_props": ["name", "title", "department", "employee_id"]},
        "Quarterly_Report": {"required_props": ["quarter", "year", "revenue", "company"]},
        "founded_by": {"domain": "Company", "range": "Person"},
        "produces": {"domain": "Company", "range": "Product"}
    }
)

# Enforce consistent structure
schema_enforcer = SchemaEnforcer()
enforced_entities = schema_enforcer.enforce_entity_schema(extracted_entities, business_schema)
enforced_relationships = schema_enforcer.enforce_relationship_schema(extracted_relations, business_schema)
```

### **Problem 2: Start with What We Already Know**
```python
from semantica.seed import SeedDataManager, KnowledgeSeeder

# Load your existing verified data
seed_manager = SeedDataManager()
seed_manager.load_products("verified_products.csv")
seed_manager.load_departments("org_chart.json")
seed_manager.load_employees("hr_database")

# Seed the knowledge graph with verified foundation
knowledge_seeder = KnowledgeSeeder()
seeded_graph = knowledge_seeder.seed_entities(seed_data, verification_rules=True)
seeded_graph = knowledge_seeder.seed_relationships(org_chart, hierarchy_rules=True)

# Build on this foundation
integrator = DataIntegrator()
final_graph = integrator.integrate_seed_with_extracted(seeded_graph, extracted_data)
```

### **Problem 3: Clean Up and Merge Duplicates**
```python
from semantica.deduplication import DuplicateDetector, EntityMerger, SimilarityEngine

# Detect semantic duplicates
duplicate_detector = DuplicateDetector()
duplicates = duplicate_detector.find_semantic_duplicates(entities)
# This will find "First Quarter Sales" and "Q1 Sales Report" as duplicates

# Advanced similarity detection
similarity_engine = SimilarityEngine()
similarity = similarity_engine.calculate_semantic_similarity("First Quarter Sales", "Q1 Sales Report")
# Returns high similarity score (e.g., 0.92)

# Merge duplicates intelligently
entity_merger = EntityMerger()
merged = entity_merger.merge_duplicates(duplicates, strategy="highest_confidence")
merged = entity_merger.merge_with_validation(duplicates, validation_rules)
```

### **Problem 4: Flag When Sources Disagree**
```python
from semantica.conflicts import ConflictDetector, SourceTracker, DisagreementResolver

# Detect conflicting information
conflict_detector = ConflictDetector()
conflicts = conflict_detector.detect_value_conflicts(entities, "sales_figure")
# Finds $10M vs $12M sales figures

# Track exact sources
source_tracker = SourceTracker()
sources = source_tracker.track_property_sources(property, "sales_figure", "$10M")
# Returns: [{"document": "Q1_Report.pdf", "page": 5, "section": "Financial Summary"}]

# Flag for investigation
disagreement_resolver = DisagreementResolver()
flagged = disagreement_resolver.flag_for_investigation(conflicts)
# Generates investigation report with exact document locations
```

### **Complete Production-Ready Solution**
```python
from semantica import Semantica
from semantica.templates import SchemaTemplate
from semantica.seed import SeedDataManager
from semantica.deduplication import DuplicateDetector, EntityMerger
from semantica.conflicts import ConflictDetector, SourceTracker
from semantica.kg_qa import KGQualityAssessor

# Initialize Semantica with quality assurance
core = Semantica(
    llm_provider="openai",
    embedding_model="text-embedding-3-large",
    vector_store="pinecone",
    graph_db="neo4j",
    quality_assurance=True,  # Enable all QA features
    conflict_detection=True,  # Enable conflict detection
    deduplication=True       # Enable advanced deduplication
)

# 1. Define your business schema
business_schema = SchemaTemplate.load("business_schema.yaml")

# 2. Seed with verified data
seed_manager = SeedDataManager()
seed_manager.load_verified_data("verified_entities.json")
seeded_graph = seed_manager.create_foundation_graph(business_schema)

# 3. Process documents with quality controls
knowledge_base = core.build_knowledge_base(
    sources=["documents/"],
    schema_template=business_schema,
    seed_data=seeded_graph,
    enable_deduplication=True,
    enable_conflict_detection=True,
    enable_quality_assurance=True
)

# 4. Get quality report
kg_qa = KGQualityAssessor()
quality_report = kg_qa.generate_quality_report(knowledge_base)
print(f"Knowledge Graph Quality Score: {quality_report.overall_score}")
print(f"Duplicates Found: {quality_report.duplicates_count}")
print(f"Conflicts Detected: {quality_report.conflicts_count}")
print(f"Source Disagreements: {quality_report.disagreements_count}")

# 5. Get investigation guide for conflicts
if quality_report.conflicts_count > 0:
    investigation_guide = quality_report.get_investigation_guide()
    print("Conflicts requiring investigation:")
    for conflict in investigation_guide.conflicts:
        print(f"- {conflict.property}: {conflict.values}")
        print(f"  Sources: {conflict.source_documents}")
```

---

*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. The toolkit specifically addresses the fundamental challenges in building Knowledge Graphs that are consistent, reliable, and production-ready.*