semantica/Modules.md

🧩 Semantica Modules & Submodules

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

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📋 Table of Contents

1. Core Modules
2. Data Processing
3. Semantic Intelligence
4. Storage & Retrieval
5. AI & Reasoning
6. Knowledge Graph Quality Assurance
7. Complete Module Index
8. Import Reference

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🏗️ Core Modules

1. Core Engine (semantica.core)

Main Class: Semantica

Purpose: Central orchestration, configuration, and pipeline management

Imports:

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

Main Functions:

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

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

Submodules with Functions:

Orchestrator (semantica.core.orchestrator):

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

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

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

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

Config Manager (semantica.core.config_manager):

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

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

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

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

Plugin Registry (semantica.core.plugin_registry):

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

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

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

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

Lifecycle (semantica.core.lifecycle):

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

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

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

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

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

2. Pipeline Builder (semantica.pipeline)

Main Class: PipelineBuilder

Purpose: Create and manage data processing pipelines

Imports:

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

Main Functions:

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

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

Submodules with Functions:

Execution Engine (semantica.pipeline.execution_engine):

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

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

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

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

Failure Handler (semantica.pipeline.failure_handler):

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

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

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

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

Parallelism Manager (semantica.pipeline.parallelism_manager):

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

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

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

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

Resource Scheduler (semantica.pipeline.resource_scheduler):

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

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

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

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

Pipeline Validator (semantica.pipeline.pipeline_validator):

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

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

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

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

Monitoring Hooks (semantica.pipeline.monitoring_hooks):

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

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

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

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

Pipeline Templates (semantica.pipeline.pipeline_templates):

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

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

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

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

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📊 Data Processing

3. Data Ingestion (semantica.ingest)

Main Classes: FileIngestor, WebIngestor, FeedIngestor

Purpose: Ingest data from various sources

Imports:

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:

# 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):

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):

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):

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):

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):

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):

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):

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:

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:

# 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):

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):

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):

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):

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):

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):

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):

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:

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:

# 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):

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):

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):

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):

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):

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):

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:

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:

# 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):

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):

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):

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):

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):

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:

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:

# 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):

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):

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):

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):

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):

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):

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:

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:

# 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):

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):

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):

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):

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):

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:

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:

# 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):

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):

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):

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):

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):

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:

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:

# 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):

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):

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):

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):

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

from semantica.triple_store import BlazegraphAdapter, JenaAdapter

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

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

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

Submodules:

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

12. Embeddings (semantica.embeddings)

Main Class: SemanticEmbedder

Purpose: Generate semantic embeddings for text and multimodal content

from semantica.embeddings import SemanticEmbedder

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

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

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

Submodules:

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

---

🤖 AI & Reasoning

13. RAG System (semantica.qa_rag)

Main Classes: RAGManager, SemanticChunker, AnswerBuilder

Purpose: Question answering and retrieval-augmented generation

Imports:

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:

# 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):

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):

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):

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):

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):

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

from semantica.reasoning import InferenceEngine, SPARQLReasoner

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

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

Submodules:

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

15. Multi-Agent System (semantica.agents)

Main Classes: AgentManager, OrchestrationEngine, MultiAgentManager

Purpose: Multi-agent coordination and workflows

from semantica.agents import AgentManager, OrchestrationEngine

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

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

Submodules:

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

---

🚀 Quick Start Examples

Complete Pipeline Example

from semantica import Semantica
from semantica.pipeline import PipelineBuilder

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

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

# Execute pipeline
results = pipeline.run()

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

---

📚 Additional Resources

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

---

🔧 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:

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:

# 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):

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):

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:

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:

# 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):

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):

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:

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:

# 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):

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):

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:

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:

# 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):

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):

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):

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:

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:

# 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):

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):

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

# =============================================================================
# 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

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

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

External Dependencies

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

# Triple stores
rdflib >= 6.0.0
sparqlwrapper >= 2.0.0

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

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

---

📊 Module Statistics

• Total Main Modules: 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

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

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

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

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

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.