diff --git a/Modules.md b/Modules.md deleted file mode 100644 index 3bb450a9..00000000 --- a/Modules.md +++ /dev/null @@ -1,3428 +0,0 @@ -# 🧩 Semantica Modules & Submodules - -> **Complete reference guide for all Semantica toolkit modules with practical code examples** - ---- - -## 📋 Table of Contents - -1. [Core Modules](#core-modules) -2. [Data Processing](#data-processing) -3. [Semantic Intelligence](#semantic-intelligence) -4. [Storage & Retrieval](#storage--retrieval) -5. [AI & Reasoning](#ai--reasoning) -6. [Knowledge Graph Quality Assurance](#knowledge-graph-quality-assurance) -7. [Complete Module Index](#complete-module-index) -8. [Import Reference](#import-reference) - ---- - -## 🏗️ Core Modules - -### 1. **Core Engine** (`semantica.core`) - -**Main Class:** `Semantica` - -**Purpose:** Central orchestration, configuration, and pipeline management - -#### **Imports:** -```python -from semantica import Semantica -from semantica.core import Config, PluginManager, Orchestrator, LifecycleManager -from semantica.core.orchestrator import PipelineCoordinator, TaskScheduler, ResourceManager -from semantica.core.config_manager import YAMLConfigParser, JSONConfigParser, EnvironmentConfig -from semantica.core.plugin_registry import PluginLoader, VersionCompatibility, DependencyResolver -from semantica.core.lifecycle import StartupHooks, ShutdownHooks, HealthChecker, GracefulDegradation -``` - -#### **Main Functions:** -```python -# Initialize Semantica with configuration -core = Semantica( - llm_provider="openai", - embedding_model="text-embedding-3-large", - vector_store="pinecone", - graph_db="neo4j" -) - -# Core functionality -core.initialize() # Setup all modules -knowledge_base = core.build_knowledge_base(sources) # Process data -status = core.get_status() # Get system health -pipeline = core.create_pipeline() # Create processing pipeline -config = core.get_config() # Get current configuration -plugins = core.list_plugins() # List available plugins -``` - -#### **Submodules with Functions:** - -**Orchestrator (`semantica.core.orchestrator`):** -```python -from semantica.core.orchestrator import PipelineCoordinator, TaskScheduler, ResourceManager - -# Pipeline coordination -coordinator = PipelineCoordinator() -coordinator.schedule_pipeline(pipeline_config) -coordinator.monitor_progress(pipeline_id) -coordinator.handle_failures(pipeline_id) - -# Task scheduling -scheduler = TaskScheduler() -scheduler.schedule_task(task, priority="high") -scheduler.get_queue_status() -scheduler.cancel_task(task_id) - -# Resource management -resource_manager = ResourceManager() -resource_manager.allocate_resources(requirements) -resource_manager.monitor_usage() -resource_manager.release_resources(resource_id) -``` - -**Config Manager (`semantica.core.config_manager`):** -```python -from semantica.core.config_manager import YAMLConfigParser, JSONConfigParser, EnvironmentConfig - -# YAML configuration -yaml_parser = YAMLConfigParser() -config = yaml_parser.load("config.yaml") -yaml_parser.validate(config, schema="config_schema.yaml") -yaml_parser.save(config, "output.yaml") - -# JSON configuration -json_parser = JSONConfigParser() -config = json_parser.load("config.json") -json_parser.merge_configs(base_config, override_config) - -# Environment configuration -env_config = EnvironmentConfig() -env_config.load_from_env() -env_config.set_defaults(defaults) -``` - -**Plugin Registry (`semantica.core.plugin_registry`):** -```python -from semantica.core.plugin_registry import PluginLoader, VersionCompatibility, DependencyResolver - -# Plugin loading -loader = PluginLoader() -plugin = loader.load_plugin("custom_processor", version="1.2.0") -loader.register_plugin(plugin) -loader.unload_plugin(plugin_id) - -# Version compatibility -version_checker = VersionCompatibility() -compatible = version_checker.check_compatibility(plugin, semantica_version) -version_checker.get_compatible_versions(plugin_name) - -# Dependency resolution -resolver = DependencyResolver() -dependencies = resolver.resolve_dependencies(plugin) -resolver.install_dependencies(dependencies) -``` - -**Lifecycle (`semantica.core.lifecycle`):** -```python -from semantica.core.lifecycle import StartupHooks, ShutdownHooks, HealthChecker, GracefulDegradation - -# Startup hooks -startup = StartupHooks() -startup.register_hook("database_init", init_database) -startup.register_hook("cache_warmup", warmup_cache) -startup.execute_hooks() - -# Shutdown hooks -shutdown = ShutdownHooks() -shutdown.register_hook("cleanup_temp", cleanup_temp_files) -shutdown.register_hook("close_connections", close_db_connections) -shutdown.execute_hooks() - -# Health checking -health = HealthChecker() -health.add_check("database", check_database_health) -health.add_check("memory", check_memory_usage) -status = health.run_checks() - -# Graceful degradation -degradation = GracefulDegradation() -degradation.set_fallback_strategy("cache_only") -degradation.handle_service_failure(service_name) -``` - -### 2. **Pipeline Builder** (`semantica.pipeline`) - -**Main Class:** `PipelineBuilder` - -**Purpose:** Create and manage data processing pipelines - -#### **Imports:** -```python -from semantica.pipeline import PipelineBuilder, ExecutionEngine, FailureHandler -from semantica.pipeline.execution_engine import PipelineRunner, StepOrchestrator, ProgressTracker -from semantica.pipeline.failure_handler import RetryHandler, FallbackHandler, ErrorRecovery -from semantica.pipeline.parallelism_manager import ParallelExecutor, LoadBalancer, TaskDistributor -from semantica.pipeline.resource_scheduler import CPUScheduler, GPUScheduler, MemoryManager -from semantica.pipeline.pipeline_validator import DependencyChecker, CycleDetector, ConfigValidator -from semantica.pipeline.monitoring_hooks import MetricsCollector, AlertManager, StatusReporter -from semantica.pipeline.pipeline_templates import PrebuiltTemplates, CustomTemplates, TemplateManager -``` - -#### **Main Functions:** -```python -# Build custom pipeline -pipeline = PipelineBuilder() \ - .add_step("ingest", {"source": "documents/"}) \ - .add_step("parse", {"formats": ["pdf", "docx"]}) \ - .add_step("extract", {"entities": True, "relations": True}) \ - .add_step("embed", {"model": "text-embedding-3-large"}) \ - .set_parallelism(4) \ - .build() - -# Execute pipeline -results = pipeline.run() -pipeline.pause() # Pause execution -pipeline.resume() # Resume execution -pipeline.stop() # Stop execution -status = pipeline.get_status() # Get current status -``` - -#### **Submodules with Functions:** - -**Execution Engine (`semantica.pipeline.execution_engine`):** -```python -from semantica.pipeline.execution_engine import PipelineRunner, StepOrchestrator, ProgressTracker - -# Pipeline execution -runner = PipelineRunner() -runner.execute_pipeline(pipeline_config) -runner.pause_pipeline(pipeline_id) -runner.resume_pipeline(pipeline_id) -runner.stop_pipeline(pipeline_id) - -# Step orchestration -orchestrator = StepOrchestrator() -orchestrator.coordinate_steps(steps) -orchestrator.manage_dependencies(step_dependencies) -orchestrator.handle_step_completion(step_id, result) - -# Progress tracking -tracker = ProgressTracker() -tracker.track_progress(pipeline_id) -tracker.get_completion_percentage() -tracker.estimate_remaining_time() -``` - -**Failure Handler (`semantica.pipeline.failure_handler`):** -```python -from semantica.pipeline.failure_handler import RetryHandler, FallbackHandler, ErrorRecovery - -# Retry logic -retry_handler = RetryHandler(max_retries=3, backoff_factor=2.0) -retry_handler.retry_failed_step(step_id, error) -retry_handler.set_retry_policy(step_type, retry_policy) - -# Fallback strategies -fallback = FallbackHandler() -fallback.set_fallback_strategy("cache_only") -fallback.handle_service_failure(service_name) -fallback.switch_to_backup(primary_failed) - -# Error recovery -recovery = ErrorRecovery() -recovery.analyze_error(error) -recovery.suggest_recovery_actions(error) -recovery.execute_recovery(recovery_plan) -``` - -**Parallelism Manager (`semantica.pipeline.parallelism_manager`):** -```python -from semantica.pipeline.parallelism_manager import ParallelExecutor, LoadBalancer, TaskDistributor - -# Parallel execution -executor = ParallelExecutor(max_workers=8) -executor.execute_parallel(tasks) -executor.set_parallelism_level(level=4) -executor.monitor_worker_health() - -# Load balancing -balancer = LoadBalancer() -balancer.distribute_load(tasks, workers) -balancer.rebalance_workload() -balancer.get_worker_utilization() - -# Task distribution -distributor = TaskDistributor() -distributor.distribute_tasks(tasks, workers) -distributor.collect_results(worker_results) -distributor.handle_worker_failure(worker_id) -``` - -**Resource Scheduler (`semantica.pipeline.resource_scheduler`):** -```python -from semantica.pipeline.resource_scheduler import CPUScheduler, GPUScheduler, MemoryManager - -# CPU scheduling -cpu_scheduler = CPUScheduler() -cpu_scheduler.allocate_cpu(cores=4) -cpu_scheduler.set_cpu_affinity(process_id, cores) -cpu_scheduler.monitor_cpu_usage() - -# GPU scheduling -gpu_scheduler = GPUScheduler() -gpu_scheduler.allocate_gpu(device_id=0) -gpu_scheduler.set_gpu_memory_limit(limit="8GB") -gpu_scheduler.monitor_gpu_usage() - -# Memory management -memory_manager = MemoryManager() -memory_manager.allocate_memory(size="2GB") -memory_manager.optimize_memory_usage() -memory_manager.garbage_collect() -``` - -**Pipeline Validator (`semantica.pipeline.pipeline_validator`):** -```python -from semantica.pipeline.pipeline_validator import DependencyChecker, CycleDetector, ConfigValidator - -# Dependency checking -dep_checker = DependencyChecker() -dep_checker.check_dependencies(pipeline_steps) -dep_checker.validate_dependency_graph(graph) -dep_checker.suggest_dependency_fixes(issues) - -# Cycle detection -cycle_detector = CycleDetector() -has_cycles = cycle_detector.detect_cycles(pipeline_graph) -cycles = cycle_detector.find_cycles(pipeline_graph) -cycle_detector.suggest_cycle_breaks(cycles) - -# Configuration validation -config_validator = ConfigValidator() -config_validator.validate_config(pipeline_config) -config_validator.check_required_fields(config) -config_validator.validate_data_types(config) -``` - -**Monitoring Hooks (`semantica.pipeline.monitoring_hooks`):** -```python -from semantica.pipeline.monitoring_hooks import MetricsCollector, AlertManager, StatusReporter - -# Metrics collection -metrics = MetricsCollector() -metrics.collect_pipeline_metrics(pipeline_id) -metrics.record_step_duration(step_id, duration) -metrics.record_memory_usage(step_id, memory) - -# Alert management -alerts = AlertManager() -alerts.set_alert_threshold("memory_usage", threshold=0.9) -alerts.send_alert("High memory usage detected") -alerts.configure_notifications(email="admin@example.com") - -# Status reporting -reporter = StatusReporter() -reporter.generate_status_report(pipeline_id) -reporter.export_metrics(format="json") -reporter.create_dashboard_data() -``` - -**Pipeline Templates (`semantica.pipeline.pipeline_templates`):** -```python -from semantica.pipeline.pipeline_templates import PrebuiltTemplates, CustomTemplates, TemplateManager - -# Prebuilt templates -templates = PrebuiltTemplates() -doc_processing = templates.get_template("document_processing") -web_scraping = templates.get_template("web_scraping") -knowledge_extraction = templates.get_template("knowledge_extraction") - -# Custom templates -custom = CustomTemplates() -custom.create_template("my_pipeline", steps) -custom.save_template(template, "my_pipeline.json") -custom.load_template("my_pipeline.json") - -# Template management -manager = TemplateManager() -manager.list_templates() -manager.validate_template(template) -manager.export_template(template_id, "export.json") -``` - ---- - -## 📊 Data Processing - -### 3. **Data Ingestion** (`semantica.ingest`) - -**Main Classes:** `FileIngestor`, `WebIngestor`, `FeedIngestor` - -**Purpose:** Ingest data from various sources - -#### **Imports:** -```python -from semantica.ingest import FileIngestor, WebIngestor, FeedIngestor -from semantica.ingest.file import LocalFileHandler, S3Handler, GCSHandler, AzureHandler -from semantica.ingest.web import WebScraper, SitemapCrawler, JavaScriptRenderer -from semantica.ingest.feed import RSSParser, AtomParser, SocialMediaAPI -from semantica.ingest.stream import WebSocketHandler, MessageQueueHandler, KafkaStream -from semantica.ingest.repo import GitHandler, PackageManagerHandler -from semantica.ingest.email import IMAPHandler, ExchangeHandler, GmailAPIHandler -from semantica.ingest.db_export import DatabaseExporter, SQLQueryHandler, ETLProcessor -``` - -#### **Main Functions:** -```python -# File ingestion -file_ingestor = FileIngestor() -files = file_ingestor.scan_directory("documents/", recursive=True) -formats = file_ingestor.detect_format("document.pdf") -metadata = file_ingestor.extract_metadata("document.pdf") -content = file_ingestor.ingest_file("document.pdf") - -# Web ingestion -web_ingestor = WebIngestor(respect_robots=True, max_depth=3) -web_content = web_ingestor.crawl_site("https://example.com") -links = web_ingestor.extract_links(web_content) -sitemap = web_ingestor.parse_sitemap("https://example.com/sitemap.xml") - -# Feed ingestion -feed_ingestor = FeedIngestor() -rss_data = feed_ingestor.parse_rss("https://example.com/feed.xml") -atom_data = feed_ingestor.parse_atom("https://example.com/atom.xml") -``` - -#### **Submodules with Functions:** - -**File Handler (`semantica.ingest.file`):** -```python -from semantica.ingest.file import LocalFileHandler, S3Handler, GCSHandler, AzureHandler - -# Local file handling -local_handler = LocalFileHandler() -files = local_handler.scan_directory("documents/", patterns=["*.pdf", "*.docx"]) -file_info = local_handler.get_file_info("document.pdf") -content = local_handler.read_file("document.pdf", encoding="utf-8") - -# S3 handling -s3_handler = S3Handler(bucket="my-bucket", region="us-east-1") -s3_files = s3_handler.list_objects(prefix="documents/") -s3_content = s3_handler.download_object("documents/file.pdf") -s3_handler.upload_object("local_file.pdf", "documents/remote_file.pdf") - -# Google Cloud Storage -gcs_handler = GCSHandler(project="my-project", bucket="my-bucket") -gcs_files = gcs_handler.list_blobs(prefix="documents/") -gcs_content = gcs_handler.download_blob("documents/file.pdf") - -# Azure Blob Storage -azure_handler = AzureHandler(account_name="myaccount", container="documents") -azure_files = azure_handler.list_blobs() -azure_content = azure_handler.download_blob("file.pdf") -``` - -**Web Scraper (`semantica.ingest.web`):** -```python -from semantica.ingest.web import WebScraper, SitemapCrawler, JavaScriptRenderer - -# Web scraping -scraper = WebScraper(respect_robots=True, delay=1.0) -content = scraper.scrape_url("https://example.com") -links = scraper.extract_links(content) -images = scraper.extract_images(content) -text = scraper.extract_text(content) - -# Sitemap crawling -sitemap_crawler = SitemapCrawler() -urls = sitemap_crawler.parse_sitemap("https://example.com/sitemap.xml") -sitemap_crawler.crawl_sitemap_urls(urls, max_pages=100) - -# JavaScript rendering -js_renderer = JavaScriptRenderer(headless=True) -rendered_content = js_renderer.render_page("https://spa-example.com") -js_renderer.wait_for_element("div.content", timeout=10) -``` - -**Feed Parser (`semantica.ingest.feed`):** -```python -from semantica.ingest.feed import RSSParser, AtomParser, SocialMediaAPI - -# RSS parsing -rss_parser = RSSParser() -rss_feed = rss_parser.parse_rss("https://example.com/feed.xml") -entries = rss_parser.get_entries(rss_feed) -rss_parser.save_entries(entries, "rss_entries.json") - -# Atom parsing -atom_parser = AtomParser() -atom_feed = atom_parser.parse_atom("https://example.com/atom.xml") -atom_entries = atom_parser.get_entries(atom_feed) - -# Social media APIs -social_api = SocialMediaAPI(platform="twitter", api_key="your_key") -tweets = social_api.fetch_posts(hashtag="#semantica", count=100) -social_api.export_posts(tweets, format="json") -``` - -**Stream Handler (`semantica.ingest.stream`):** -```python -from semantica.ingest.stream import WebSocketHandler, MessageQueueHandler, KafkaStream - -# WebSocket streaming -ws_handler = WebSocketHandler(url="wss://example.com/stream") -ws_handler.connect() -messages = ws_handler.listen_for_messages(callback=process_message) -ws_handler.send_message({"type": "subscribe", "channel": "updates"}) - -# Message queue handling -mq_handler = MessageQueueHandler(queue_type="rabbitmq", host="localhost") -mq_handler.connect() -mq_handler.consume_messages(queue="data_queue", callback=process_message) -mq_handler.publish_message("data_queue", {"data": "new_content"}) - -# Kafka streaming -kafka_stream = KafkaStream(bootstrap_servers=["localhost:9092"]) -kafka_stream.subscribe_topics(["data_topic"]) -kafka_stream.consume_messages(callback=process_kafka_message) -kafka_stream.produce_message("data_topic", {"key": "value"}) -``` - -**Repository Handler (`semantica.ingest.repo`):** -```python -from semantica.ingest.repo import GitHandler, PackageManagerHandler - -# Git repository handling -git_handler = GitHandler() -git_handler.clone_repository("https://github.com/user/repo.git", "local_repo") -commits = git_handler.get_commits(since="2023-01-01") -files = git_handler.get_changed_files(commit_hash="abc123") -git_handler.checkout_branch("feature-branch") - -# Package manager handling -pkg_handler = PackageManagerHandler(manager="npm") -packages = pkg_handler.list_packages() -pkg_handler.install_package("package-name", version="1.0.0") -pkg_info = pkg_handler.get_package_info("package-name") -``` - -**Email Handler (`semantica.ingest.email`):** -```python -from semantica.ingest.email import IMAPHandler, ExchangeHandler, GmailAPIHandler - -# IMAP handling -imap_handler = IMAPHandler(server="imap.gmail.com", port=993) -imap_handler.connect(username="user@example.com", password="password") -emails = imap_handler.fetch_emails(folder="INBOX", since="2023-01-01") -attachments = imap_handler.download_attachments(email_id=123) - -# Exchange handling -exchange_handler = ExchangeHandler(server="outlook.office365.com") -exchange_handler.connect(username="user@example.com", password="password") -exchange_emails = exchange_handler.get_emails(folder="Inbox") - -# Gmail API handling -gmail_handler = GmailAPIHandler(credentials_file="credentials.json") -gmail_emails = gmail_handler.list_messages(query="is:unread") -gmail_handler.download_attachments(message_id="msg123") -``` - -**Database Export (`semantica.ingest.db_export`):** -```python -from semantica.ingest.db_export import DatabaseExporter, SQLQueryHandler, ETLProcessor - -# Database export -db_exporter = DatabaseExporter(connection_string="postgresql://user:pass@localhost/db") -tables = db_exporter.export_table("users", output_format="csv") -db_exporter.export_schema(output_file="schema.sql") - -# SQL query handling -sql_handler = SQLQueryHandler(connection_string="postgresql://user:pass@localhost/db") -results = sql_handler.execute_query("SELECT * FROM users WHERE active = true") -sql_handler.export_query_results(results, "active_users.csv") - -# ETL processing -etl_processor = ETLProcessor() -etl_processor.extract_from_source("database", config=db_config) -etl_processor.transform_data(transform_rules=transformation_rules) -etl_processor.load_to_destination("data_warehouse", config=dw_config) -``` - -### 4. **Document Parsing** (`semantica.parse`) - -**Main Classes:** `PDFParser`, `DOCXParser`, `HTMLParser`, `ImageParser` - -**Purpose:** Extract content from various document formats - -#### **Imports:** -```python -from semantica.parse import PDFParser, DOCXParser, HTMLParser, ImageParser -from semantica.parse.pdf import PDFTextExtractor, PDFTableExtractor, PDFImageExtractor, PDFAnnotationExtractor -from semantica.parse.docx import DOCXTextExtractor, DOCXStyleExtractor, DOCXTrackChangesExtractor -from semantica.parse.pptx import PPTXSlideExtractor, PPTXNotesExtractor, PPTXMediaExtractor -from semantica.parse.excel import ExcelDataExtractor, ExcelFormulaExtractor, ExcelChartExtractor -from semantica.parse.html import HTMLDOMParser, HTMLMetadataExtractor, HTMLFormExtractor -from semantica.parse.images import OCRProcessor, ObjectDetector, EXIFExtractor -from semantica.parse.tables import TableDetector, TableStructureAnalyzer, TableDataExtractor -``` - -#### **Main Functions:** -```python -# PDF parsing -pdf_parser = PDFParser() -pdf_text = pdf_parser.extract_text("document.pdf") -pdf_tables = pdf_parser.extract_tables("document.pdf") -pdf_images = pdf_parser.extract_images("document.pdf") -pdf_annotations = pdf_parser.extract_annotations("document.pdf") - -# DOCX parsing -docx_parser = DOCXParser() -docx_content = docx_parser.get_document_structure("document.docx") -track_changes = docx_parser.extract_track_changes("document.docx") -styles = docx_parser.extract_styles("document.docx") - -# HTML parsing -html_parser = HTMLParser() -dom_tree = html_parser.parse_dom("https://example.com") -metadata = html_parser.extract_metadata(dom_tree) -forms = html_parser.extract_forms(dom_tree) - -# Image parsing (OCR) -image_parser = ImageParser() -ocr_text = image_parser.ocr_text("image.png") -objects = image_parser.detect_objects("image.jpg") -exif_data = image_parser.extract_exif("image.jpg") -``` - -#### **Submodules with Functions:** - -**PDF Parser (`semantica.parse.pdf`):** -```python -from semantica.parse.pdf import PDFTextExtractor, PDFTableExtractor, PDFImageExtractor, PDFAnnotationExtractor - -# Text extraction -text_extractor = PDFTextExtractor() -text = text_extractor.extract_text("document.pdf") -text_by_page = text_extractor.extract_text_by_page("document.pdf") -text_with_coordinates = text_extractor.extract_text_with_coordinates("document.pdf") - -# Table extraction -table_extractor = PDFTableExtractor() -tables = table_extractor.extract_tables("document.pdf") -table_data = table_extractor.extract_table_data("document.pdf", page=1) -table_structure = table_extractor.analyze_table_structure("document.pdf") - -# Image extraction -image_extractor = PDFImageExtractor() -images = image_extractor.extract_images("document.pdf") -image_metadata = image_extractor.get_image_metadata("document.pdf") -image_extractor.save_images("document.pdf", output_dir="extracted_images/") - -# Annotation extraction -annotation_extractor = PDFAnnotationExtractor() -annotations = annotation_extractor.extract_annotations("document.pdf") -comments = annotation_extractor.extract_comments("document.pdf") -highlights = annotation_extractor.extract_highlights("document.pdf") -``` - -**DOCX Parser (`semantica.parse.docx`):** -```python -from semantica.parse.docx import DOCXTextExtractor, DOCXStyleExtractor, DOCXTrackChangesExtractor - -# Text extraction -docx_text = DOCXTextExtractor() -text = docx_text.extract_text("document.docx") -paragraphs = docx_text.extract_paragraphs("document.docx") -headers_footers = docx_text.extract_headers_footers("document.docx") - -# Style extraction -style_extractor = DOCXStyleExtractor() -styles = style_extractor.extract_styles("document.docx") -formatting = style_extractor.extract_formatting("document.docx") -tables = style_extractor.extract_table_styles("document.docx") - -# Track changes extraction -track_changes = DOCXTrackChangesExtractor() -changes = track_changes.extract_changes("document.docx") -revisions = track_changes.extract_revisions("document.docx") -comments = track_changes.extract_comments("document.docx") -``` - -**PowerPoint Parser (`semantica.parse.pptx`):** -```python -from semantica.parse.pptx import PPTXSlideExtractor, PPTXNotesExtractor, PPTXMediaExtractor - -# Slide extraction -slide_extractor = PPTXSlideExtractor() -slides = slide_extractor.extract_slides("presentation.pptx") -slide_text = slide_extractor.extract_slide_text("presentation.pptx", slide_number=1) -slide_layout = slide_extractor.extract_slide_layout("presentation.pptx") - -# Notes extraction -notes_extractor = PPTXNotesExtractor() -notes = notes_extractor.extract_notes("presentation.pptx") -speaker_notes = notes_extractor.extract_speaker_notes("presentation.pptx") - -# Media extraction -media_extractor = PPTXMediaExtractor() -images = media_extractor.extract_images("presentation.pptx") -videos = media_extractor.extract_videos("presentation.pptx") -audio = media_extractor.extract_audio("presentation.pptx") -``` - -**Excel Parser (`semantica.parse.excel`):** -```python -from semantica.parse.excel import ExcelDataExtractor, ExcelFormulaExtractor, ExcelChartExtractor - -# Data extraction -excel_extractor = ExcelDataExtractor() -data = excel_extractor.extract_data("spreadsheet.xlsx", sheet_name="Sheet1") -all_sheets = excel_extractor.extract_all_sheets("spreadsheet.xlsx") -cell_values = excel_extractor.extract_cell_values("spreadsheet.xlsx", range="A1:C10") - -# Formula extraction -formula_extractor = ExcelFormulaExtractor() -formulas = formula_extractor.extract_formulas("spreadsheet.xlsx") -formula_dependencies = formula_extractor.analyze_formula_dependencies("spreadsheet.xlsx") - -# Chart extraction -chart_extractor = ExcelChartExtractor() -charts = chart_extractor.extract_charts("spreadsheet.xlsx") -chart_data = chart_extractor.extract_chart_data("spreadsheet.xlsx", chart_name="Chart1") -``` - -**HTML Parser (`semantica.parse.html`):** -```python -from semantica.parse.html import HTMLDOMParser, HTMLMetadataExtractor, HTMLFormExtractor - -# DOM parsing -dom_parser = HTMLDOMParser() -dom_tree = dom_parser.parse_dom("https://example.com") -elements = dom_parser.find_elements(dom_tree, tag="div", class_name="content") -links = dom_parser.extract_links(dom_tree) -images = dom_parser.extract_images(dom_tree) - -# Metadata extraction -metadata_extractor = HTMLMetadataExtractor() -title = metadata_extractor.extract_title(dom_tree) -description = metadata_extractor.extract_description(dom_tree) -keywords = metadata_extractor.extract_keywords(dom_tree) -og_data = metadata_extractor.extract_og_metadata(dom_tree) - -# Form extraction -form_extractor = HTMLFormExtractor() -forms = form_extractor.extract_forms(dom_tree) -form_fields = form_extractor.extract_form_fields(dom_tree) -form_actions = form_extractor.extract_form_actions(dom_tree) -``` - -**Image Parser (`semantica.parse.images`):** -```python -from semantica.parse.images import OCRProcessor, ObjectDetector, EXIFExtractor - -# OCR processing -ocr_processor = OCRProcessor() -text = ocr_processor.extract_text("image.png") -text_confidence = ocr_processor.extract_text_with_confidence("image.png") -text_by_region = ocr_processor.extract_text_by_region("image.png", regions=[(0,0,100,100)]) - -# Object detection -object_detector = ObjectDetector() -objects = object_detector.detect_objects("image.jpg") -faces = object_detector.detect_faces("image.jpg") -text_regions = object_detector.detect_text_regions("image.jpg") - -# EXIF data extraction -exif_extractor = EXIFExtractor() -exif_data = exif_extractor.extract_exif("image.jpg") -camera_info = exif_extractor.extract_camera_info("image.jpg") -location_data = exif_extractor.extract_location("image.jpg") -``` - -**Table Parser (`semantica.parse.tables`):** -```python -from semantica.parse.tables import TableDetector, TableStructureAnalyzer, TableDataExtractor - -# Table detection -table_detector = TableDetector() -tables = table_detector.detect_tables("document.pdf") -table_regions = table_detector.detect_table_regions("document.pdf") - -# Structure analysis -structure_analyzer = TableStructureAnalyzer() -structure = structure_analyzer.analyze_structure("document.pdf", table_region) -headers = structure_analyzer.detect_headers("document.pdf", table_region) -rows_columns = structure_analyzer.detect_rows_columns("document.pdf", table_region) - -# Data extraction -data_extractor = TableDataExtractor() -table_data = data_extractor.extract_data("document.pdf", table_region) -csv_data = data_extractor.extract_as_csv("document.pdf", table_region) -json_data = data_extractor.extract_as_json("document.pdf", table_region) -``` - -### 5. **Text Normalization** (`semantica.normalize`) - -**Main Classes:** `TextCleaner`, `LanguageDetector`, `EntityNormalizer` - -**Purpose:** Clean and normalize text data - -#### **Imports:** -```python -from semantica.normalize import TextCleaner, LanguageDetector, EntityNormalizer -from semantica.normalize.text_cleaner import HTMLRemover, WhitespaceNormalizer, SpecialCharRemover -from semantica.normalize.language_detector import MultiLanguageDetector, LanguageConfidence, LanguageSupport -from semantica.normalize.encoding_handler import UTF8Converter, EncodingValidator, EncodingDetector -from semantica.normalize.entity_normalizer import EntityCanonicalizer, AcronymExpander, EntityStandardizer -from semantica.normalize.date_normalizer import DateFormatter, DateParser, DateValidator -from semantica.normalize.number_normalizer import NumberFormatter, NumberParser, CurrencyNormalizer -``` - -#### **Main Functions:** -```python -# Text cleaning -cleaner = TextCleaner() -clean_text = cleaner.remove_html(html_content) -normalized = cleaner.normalize_whitespace(text) -cleaned = cleaner.remove_special_chars(text) -unicode_normalized = cleaner.normalize_unicode(text) - -# Language detection -detector = LanguageDetector() -language = detector.detect("Hello world") -confidence = detector.get_confidence() -supported = detector.supported_languages() -multi_lang = detector.detect_multiple_languages(mixed_text) - -# Entity normalization -normalizer = EntityNormalizer() -canonical = normalizer.canonicalize("Apple Inc.", "Apple") -expanded = normalizer.expand_acronyms("NASA") -standardized = normalizer.standardize_entities(text) -``` - -#### **Submodules with Functions:** - -**Text Cleaner (`semantica.normalize.text_cleaner`):** -```python -from semantica.normalize.text_cleaner import HTMLRemover, WhitespaceNormalizer, SpecialCharRemover - -# HTML removal -html_remover = HTMLRemover() -clean_text = html_remover.remove_html("

Hello world

") -clean_text = html_remover.remove_html_tags(html_content, keep_tags=["p", "br"]) -clean_text = html_remover.remove_html_entities("& < >") - -# Whitespace normalization -whitespace_normalizer = WhitespaceNormalizer() -normalized = whitespace_normalizer.normalize_spaces(" multiple spaces ") -normalized = whitespace_normalizer.normalize_line_breaks(text) -normalized = whitespace_normalizer.remove_extra_whitespace(text) - -# Special character removal -char_remover = SpecialCharRemover() -cleaned = char_remover.remove_special_chars("Text with @#$% symbols") -cleaned = char_remover.remove_control_chars(text) -cleaned = char_remover.remove_non_printable(text) -``` - -**Language Detector (`semantica.normalize.language_detector`):** -```python -from semantica.normalize.language_detector import MultiLanguageDetector, LanguageConfidence, LanguageSupport - -# Multi-language detection -multi_detector = MultiLanguageDetector() -languages = multi_detector.detect_languages("Hello world. Bonjour le monde.") -primary_lang = multi_detector.get_primary_language(text) -language_confidence = multi_detector.get_confidence_scores(text) - -# Language confidence -confidence_calculator = LanguageConfidence() -confidence = confidence_calculator.calculate_confidence(text, "en") -confidence_scores = confidence_calculator.get_all_confidence_scores(text) - -# Language support -language_support = LanguageSupport() -supported = language_support.get_supported_languages() -is_supported = language_support.is_language_supported("en") -language_info = language_support.get_language_info("en") -``` - -**Encoding Handler (`semantica.normalize.encoding_handler`):** -```python -from semantica.normalize.encoding_handler import UTF8Converter, EncodingValidator, EncodingDetector - -# UTF-8 conversion -utf8_converter = UTF8Converter() -utf8_text = utf8_converter.convert_to_utf8(text, source_encoding="latin1") -utf8_text = utf8_converter.ensure_utf8(text) -utf8_text = utf8_converter.fix_encoding_issues(text) - -# Encoding validation -encoding_validator = EncodingValidator() -is_valid = encoding_validator.validate_encoding(text, "utf-8") -validation_report = encoding_validator.validate_text_encoding(text) -encoding_validator.fix_encoding_errors(text) - -# Encoding detection -encoding_detector = EncodingDetector() -detected_encoding = encoding_detector.detect_encoding(text) -confidence = encoding_detector.get_detection_confidence() -possible_encodings = encoding_detector.get_possible_encodings(text) -``` - -**Entity Normalizer (`semantica.normalize.entity_normalizer`):** -```python -from semantica.normalize.entity_normalizer import EntityCanonicalizer, AcronymExpander, EntityStandardizer - -# Entity canonicalization -canonicalizer = EntityCanonicalizer() -canonical = canonicalizer.canonicalize("Apple Inc.", "Apple") -canonical = canonicalizer.canonicalize_entities(text) -canonical = canonicalizer.merge_duplicate_entities(entities) - -# Acronym expansion -acronym_expander = AcronymExpander() -expanded = acronym_expander.expand_acronyms("NASA", context="space agency") -expanded = acronym_expander.expand_all_acronyms(text) -acronym_map = acronym_expander.build_acronym_dictionary(text) - -# Entity standardization -entity_standardizer = EntityStandardizer() -standardized = entity_standardizer.standardize_entities(text) -standardized = entity_standardizer.normalize_entity_names(entities) -standardized = entity_standardizer.validate_entity_format(entities) -``` - -**Date Normalizer (`semantica.normalize.date_normalizer`):** -```python -from semantica.normalize.date_normalizer import DateFormatter, DateParser, DateValidator - -# Date formatting -date_formatter = DateFormatter() -formatted = date_formatter.format_date("2023-01-15", format="%B %d, %Y") -formatted = date_formatter.normalize_date_format("15/01/2023") -formatted = date_formatter.standardize_dates(text) - -# Date parsing -date_parser = DateParser() -parsed = date_parser.parse_date("January 15, 2023") -parsed = date_parser.parse_flexible_date("15th Jan 2023") -parsed = date_parser.extract_dates_from_text(text) - -# Date validation -date_validator = DateValidator() -is_valid = date_validator.validate_date("2023-01-15") -is_valid = date_validator.validate_date_range(start_date, end_date) -validation_report = date_validator.validate_dates_in_text(text) -``` - -**Number Normalizer (`semantica.normalize.number_normalizer`):** -```python -from semantica.normalize.number_normalizer import NumberFormatter, NumberParser, CurrencyNormalizer - -# Number formatting -number_formatter = NumberFormatter() -formatted = number_formatter.format_number(1234567.89, format="comma") -formatted = number_formatter.normalize_numbers(text) -formatted = number_formatter.standardize_decimal_separator(text) - -# Number parsing -number_parser = NumberParser() -parsed = number_parser.parse_number("1,234,567.89") -parsed = number_parser.parse_flexible_number("1.23M") -parsed = number_parser.extract_numbers_from_text(text) - -# Currency normalization -currency_normalizer = CurrencyNormalizer() -normalized = currency_normalizer.normalize_currency("$1,234.56", target_currency="USD") -normalized = currency_normalizer.convert_currency(amount, from_currency="EUR", to_currency="USD") -normalized = currency_normalizer.standardize_currency_format(text) -``` - -### 6. **Text Chunking** (`semantica.split`) - -**Main Classes:** `SemanticChunker`, `StructuralChunker`, `TableChunker` - -**Purpose:** Split documents into optimal chunks for processing - -#### **Imports:** -```python -from semantica.split import SemanticChunker, StructuralChunker, TableChunker -from semantica.split.sliding_window import SlidingWindowChunker, OverlapChunker, FixedSizeChunker -from semantica.split.semantic_chunker import TopicBasedChunker, SentenceChunker, ParagraphChunker -from semantica.split.structural_chunker import SectionChunker, HeaderChunker, DocumentChunker -from semantica.split.table_chunker import TablePreservingChunker, TableContextExtractor, TableAwareChunker -from semantica.split.provenance_tracker import ChunkProvenanceTracker, SourceTracker, MetadataTracker -``` - -#### **Main Functions:** -```python -# Semantic chunking -semantic_chunker = SemanticChunker() -chunks = semantic_chunker.split_by_meaning(long_text) -topics = semantic_chunker.detect_topics(text) -semantic_chunks = semantic_chunker.split_by_semantic_similarity(text) - -# Structural chunking -structural_chunker = StructuralChunker() -sections = structural_chunker.split_by_sections(document) -headers = structural_chunker.identify_headers(document) -paragraphs = structural_chunker.split_by_paragraphs(document) - -# Table-aware chunking -table_chunker = TableChunker() -table_chunks = table_chunker.preserve_tables(document) -context = table_chunker.extract_table_context(table) -table_aware_chunks = table_chunker.split_with_table_context(document) -``` - -#### **Submodules with Functions:** - -**Sliding Window (`semantica.split.sliding_window`):** -```python -from semantica.split.sliding_window import SlidingWindowChunker, OverlapChunker, FixedSizeChunker - -# Sliding window chunking -sliding_chunker = SlidingWindowChunker(window_size=512, step_size=256) -chunks = sliding_chunker.chunk_text(long_text) -chunks = sliding_chunker.chunk_with_overlap(text, overlap_ratio=0.5) - -# Overlap chunking -overlap_chunker = OverlapChunker(chunk_size=500, overlap_size=100) -chunks = overlap_chunker.chunk_text(text) -chunks = overlap_chunker.chunk_with_context(text, context_size=50) - -# Fixed size chunking -fixed_chunker = FixedSizeChunker(chunk_size=1000) -chunks = fixed_chunker.chunk_text(text) -chunks = fixed_chunker.chunk_by_tokens(text, max_tokens=500) -``` - -**Semantic Chunker (`semantica.split.semantic_chunker`):** -```python -from semantica.split.semantic_chunker import TopicBasedChunker, SentenceChunker, ParagraphChunker - -# Topic-based chunking -topic_chunker = TopicBasedChunker() -chunks = topic_chunker.split_by_topics(text) -topics = topic_chunker.detect_topic_boundaries(text) -chunks = topic_chunker.split_by_topic_shift(text, threshold=0.7) - -# Sentence chunking -sentence_chunker = SentenceChunker() -chunks = sentence_chunker.split_by_sentences(text) -chunks = sentence_chunker.split_by_sentence_similarity(text) -chunks = sentence_chunker.split_by_semantic_coherence(text) - -# Paragraph chunking -paragraph_chunker = ParagraphChunker() -chunks = paragraph_chunker.split_by_paragraphs(text) -chunks = paragraph_chunker.split_by_paragraph_similarity(text) -chunks = paragraph_chunker.split_by_paragraph_length(text, max_length=1000) -``` - -**Structural Chunker (`semantica.split.structural_chunker`):** -```python -from semantica.split.structural_chunker import SectionChunker, HeaderChunker, DocumentChunker - -# Section-based chunking -section_chunker = SectionChunker() -chunks = section_chunker.split_by_sections(document) -sections = section_chunker.identify_sections(document) -chunks = section_chunker.split_by_section_hierarchy(document) - -# Header-based chunking -header_chunker = HeaderChunker() -chunks = header_chunker.split_by_headers(document) -headers = header_chunker.extract_headers(document) -chunks = header_chunker.split_by_header_level(document, level=2) - -# Document-aware chunking -document_chunker = DocumentChunker() -chunks = document_chunker.split_by_document_structure(document) -chunks = document_chunker.split_by_document_type(document, doc_type="research_paper") -chunks = document_chunker.split_by_document_sections(document) -``` - -**Table Chunker (`semantica.split.table_chunker`):** -```python -from semantica.split.table_chunker import TablePreservingChunker, TableContextExtractor, TableAwareChunker - -# Table-preserving chunking -table_preserver = TablePreservingChunker() -chunks = table_preserver.split_preserve_tables(document) -chunks = table_preserver.split_around_tables(document, context_size=200) -chunks = table_preserver.split_table_aware(document) - -# Table context extraction -context_extractor = TableContextExtractor() -context = context_extractor.extract_table_context(table) -context = context_extractor.extract_table_metadata(table) -context = context_extractor.extract_table_relationships(table) - -# Table-aware chunking -table_aware = TableAwareChunker() -chunks = table_aware.split_with_table_context(document) -chunks = table_aware.split_table_centered(document) -chunks = table_aware.split_table_relationships(document) -``` - -**Provenance Tracker (`semantica.split.provenance_tracker`):** -```python -from semantica.split.provenance_tracker import ChunkProvenanceTracker, SourceTracker, MetadataTracker - -# Chunk provenance tracking -provenance_tracker = ChunkProvenanceTracker() -chunks = provenance_tracker.track_chunk_sources(text, source="document.pdf") -chunks = provenance_tracker.track_chunk_metadata(chunks, metadata={"page": 1, "section": "intro"}) -provenance = provenance_tracker.get_chunk_provenance(chunk_id) - -# Source tracking -source_tracker = SourceTracker() -sources = source_tracker.track_sources(chunks) -sources = source_tracker.track_source_hierarchy(chunks) -sources = source_tracker.track_source_relationships(chunks) - -# Metadata tracking -metadata_tracker = MetadataTracker() -metadata = metadata_tracker.track_metadata(chunks, metadata_schema) -metadata = metadata_tracker.track_metadata_changes(chunks, original_metadata) -metadata = metadata_tracker.track_metadata_provenance(chunks) -``` - ---- - -## 🧠 Semantic Intelligence - -### 7. **Semantic Extraction** (`semantica.semantic_extract`) - -**Main Classes:** `NERExtractor`, `RelationExtractor`, `TripleExtractor` - -**Purpose:** Extract semantic information from text - -#### **Imports:** -```python -from semantica.semantic_extract import NERExtractor, RelationExtractor, TripleExtractor -from semantica.semantic_extract.ner_extractor import NERModel, EntityClassifier, EntityLinker -from semantica.semantic_extract.relation_extractor import RelationModel, RelationClassifier, RelationValidator -from semantica.semantic_extract.event_detector import EventDetector, TemporalExtractor, EventClassifier -from semantica.semantic_extract.coref_resolver import CorefResolver, EntityLinker, MentionResolver -from semantica.semantic_extract.triple_extractor import TripleModel, TripleValidator, TripleFormatter -from semantica.semantic_extract.llm_enhancer import LLMEnhancer, ComplexExtractor, LLMValidator -``` - -#### **Main Functions:** -```python -# Named Entity Recognition -ner = NERExtractor() -entities = ner.extract_entities("Apple Inc. was founded by Steve Jobs in 1976") -classified = ner.classify_entities(entities) -linked = ner.link_entities(entities) - -# Relation Extraction -rel_extractor = RelationExtractor() -relations = rel_extractor.find_relations("Apple Inc. was founded by Steve Jobs") -classified_rels = rel_extractor.classify_relations(relations) -validated_rels = rel_extractor.validate_relations(relations) - -# Triple Extraction -triple_extractor = TripleExtractor() -triples = triple_extractor.extract_triples(text) -validated = triple_extractor.validate_triples(triples) -formatted = triple_extractor.format_triples(triples) - -# Export triples -turtle = triple_extractor.to_turtle(triples) -jsonld = triple_extractor.to_jsonld(triples) -ntriples = triple_extractor.to_ntriples(triples) -``` - -#### **Submodules with Functions:** - -**NER Extractor (`semantica.semantic_extract.ner_extractor`):** -```python -from semantica.semantic_extract.ner_extractor import NERModel, EntityClassifier, EntityLinker - -# NER model -ner_model = NERModel(model_name="spacy", language="en") -entities = ner_model.extract_entities(text) -entities = ner_model.extract_entities_batch(texts) -entities = ner_model.extract_entities_with_confidence(text) - -# Entity classification -entity_classifier = EntityClassifier() -classified = entity_classifier.classify_entities(entities) -classified = entity_classifier.classify_by_type(entities, entity_type="PERSON") -classified = entity_classifier.classify_by_domain(entities, domain="technology") - -# Entity linking -entity_linker = EntityLinker() -linked = entity_linker.link_to_wikidata(entities) -linked = entity_linker.link_to_dbpedia(entities) -linked = entity_linker.link_to_custom_kb(entities, knowledge_base) -``` - -**Relation Extractor (`semantica.semantic_extract.relation_extractor`):** -```python -from semantica.semantic_extract.relation_extractor import RelationModel, RelationClassifier, RelationValidator - -# Relation model -relation_model = RelationModel(model_name="rebel", language="en") -relations = relation_model.extract_relations(text) -relations = relation_model.extract_relations_batch(texts) -relations = relation_model.extract_relations_with_confidence(text) - -# Relation classification -relation_classifier = RelationClassifier() -classified = relation_classifier.classify_relations(relations) -classified = relation_classifier.classify_by_type(relations, relation_type="founded_by") -classified = relation_classifier.classify_by_domain(relations, domain="business") - -# Relation validation -relation_validator = RelationValidator() -validated = relation_validator.validate_relations(relations) -validated = relation_validator.validate_by_schema(relations, schema) -validated = relation_validator.validate_by_consistency(relations) -``` - -**Event Detector (`semantica.semantic_extract.event_detector`):** -```python -from semantica.semantic_extract.event_detector import EventDetector, TemporalExtractor, EventClassifier - -# Event detection -event_detector = EventDetector() -events = event_detector.detect_events(text) -events = event_detector.detect_events_batch(texts) -events = event_detector.detect_events_with_confidence(text) - -# Temporal extraction -temporal_extractor = TemporalExtractor() -temporal = temporal_extractor.extract_temporal_expressions(text) -temporal = temporal_extractor.extract_dates(text) -temporal = temporal_extractor.extract_time_expressions(text) - -# Event classification -event_classifier = EventClassifier() -classified = event_classifier.classify_events(events) -classified = event_classifier.classify_by_type(events, event_type="founding") -classified = event_classifier.classify_by_domain(events, domain="business") -``` - -**Coreference Resolver (`semantica.semantic_extract.coref_resolver`):** -```python -from semantica.semantic_extract.coref_resolver import CorefResolver, EntityLinker, MentionResolver - -# Coreference resolution -coref_resolver = CorefResolver() -resolved = coref_resolver.resolve_coreferences(text) -resolved = coref_resolver.resolve_coreferences_batch(texts) -resolved = coref_resolver.resolve_coreferences_with_confidence(text) - -# Entity linking -entity_linker = EntityLinker() -linked = entity_linker.link_mentions(mentions) -linked = entity_linker.link_to_knowledge_base(mentions, kb) -linked = entity_linker.link_by_similarity(mentions, threshold=0.8) - -# Mention resolution -mention_resolver = MentionResolver() -resolved = mention_resolver.resolve_mentions(mentions) -resolved = mention_resolver.resolve_pronouns(text) -resolved = mention_resolver.resolve_nominal_mentions(text) -``` - -**Triple Extractor (`semantica.semantic_extract.triple_extractor`):** -```python -from semantica.semantic_extract.triple_extractor import TripleModel, TripleValidator, TripleFormatter - -# Triple extraction -triple_model = TripleModel() -triples = triple_model.extract_triples(text) -triples = triple_model.extract_triples_batch(texts) -triples = triple_model.extract_triples_with_confidence(text) - -# Triple validation -triple_validator = TripleValidator() -validated = triple_validator.validate_triples(triples) -validated = triple_validator.validate_by_schema(triples, schema) -validated = triple_validator.validate_by_consistency(triples) - -# Triple formatting -triple_formatter = TripleFormatter() -formatted = triple_formatter.format_as_rdf(triples) -formatted = triple_formatter.format_as_jsonld(triples) -formatted = triple_formatter.format_as_turtle(triples) -``` - -**LLM Enhancer (`semantica.semantic_extract.llm_enhancer`):** -```python -from semantica.semantic_extract.llm_enhancer import LLMEnhancer, ComplexExtractor, LLMValidator - -# LLM enhancement -llm_enhancer = LLMEnhancer(model="gpt-4") -enhanced = llm_enhancer.enhance_extraction(text, extraction_type="entities") -enhanced = llm_enhancer.enhance_relations(text, relations) -enhanced = llm_enhancer.enhance_triples(text, triples) - -# Complex extraction -complex_extractor = ComplexExtractor() -complex_entities = complex_extractor.extract_complex_entities(text) -complex_relations = complex_extractor.extract_complex_relations(text) -complex_events = complex_extractor.extract_complex_events(text) - -# LLM validation -llm_validator = LLMValidator() -validated = llm_validator.validate_with_llm(extractions, text) -validated = llm_validator.validate_consistency(extractions) -validated = llm_validator.validate_completeness(extractions, text) -``` - -### 8. **Ontology Generation** (`semantica.ontology`) - -**Main Class:** `OntologyGenerator` - -**Purpose:** Generate ontologies from extracted data - -#### **Imports:** -```python -from semantica.ontology import OntologyGenerator -from semantica.ontology.class_inferrer import ClassInferrer, HierarchyBuilder, ClassValidator -from semantica.ontology.property_generator import PropertyInferrer, DataTypeInferrer, PropertyValidator -from semantica.ontology.owl_generator import OWLGenerator, RDFGenerator, TurtleGenerator -from semantica.ontology.base_mapper import SchemaMapper, FOAFMapper, DublinCoreMapper -from semantica.ontology.version_manager import VersionManager, MigrationManager, OntologyVersioner -``` - -#### **Main Functions:** -```python -# Initialize ontology generator -ontology_gen = OntologyGenerator( - base_ontologies=["schema.org", "foaf", "dublin_core"], - generate_classes=True, - generate_properties=True -) - -# Generate ontology from documents -ontology = ontology_gen.generate_from_documents(documents) -ontology = ontology_gen.generate_from_entities(entities) -ontology = ontology_gen.generate_from_triples(triples) - -# Export in various formats -owl_ontology = ontology.to_owl() -rdf_ontology = ontology.to_rdf() -turtle_ontology = ontology.to_turtle() -jsonld_ontology = ontology.to_jsonld() - -# Save to triple store -ontology.save_to_triple_store("http://localhost:9999/blazegraph/sparql") -``` - -#### **Submodules with Functions:** - -**Class Inferrer (`semantica.ontology.class_inferrer`):** -```python -from semantica.ontology.class_inferrer import ClassInferrer, HierarchyBuilder, ClassValidator - -# Class inference -class_inferrer = ClassInferrer() -classes = class_inferrer.infer_classes(entities) -classes = class_inferrer.infer_classes_from_triples(triples) -classes = class_inferrer.infer_classes_from_text(text) - -# Hierarchy building -hierarchy_builder = HierarchyBuilder() -hierarchy = hierarchy_builder.build_hierarchy(classes) -hierarchy = hierarchy_builder.build_is_a_hierarchy(classes) -hierarchy = hierarchy_builder.build_part_of_hierarchy(classes) - -# Class validation -class_validator = ClassValidator() -validated = class_validator.validate_classes(classes) -validated = class_validator.validate_hierarchy(hierarchy) -validated = class_validator.validate_consistency(classes) -``` - -**Property Generator (`semantica.ontology.property_generator`):** -```python -from semantica.ontology.property_generator import PropertyInferrer, DataTypeInferrer, PropertyValidator - -# Property inference -property_inferrer = PropertyInferrer() -properties = property_inferrer.infer_properties(entities) -properties = property_inferrer.infer_properties_from_triples(triples) -properties = property_inferrer.infer_properties_from_text(text) - -# Data type inference -datatype_inferrer = DataTypeInferrer() -datatypes = datatype_inferrer.infer_datatypes(properties) -datatypes = datatype_inferrer.infer_datatypes_from_values(values) -datatypes = datatype_inferrer.infer_datatypes_from_schema(schema) - -# Property validation -property_validator = PropertyValidator() -validated = property_validator.validate_properties(properties) -validated = property_validator.validate_datatypes(properties, datatypes) -validated = property_validator.validate_domain_range(properties) -``` - -**OWL Generator (`semantica.ontology.owl_generator`):** -```python -from semantica.ontology.owl_generator import OWLGenerator, RDFGenerator, TurtleGenerator - -# OWL generation -owl_generator = OWLGenerator() -owl_ontology = owl_generator.generate_owl(classes, properties) -owl_ontology = owl_generator.generate_owl_from_triples(triples) -owl_ontology = owl_generator.generate_owl_with_axioms(classes, properties, axioms) - -# RDF generation -rdf_generator = RDFGenerator() -rdf_ontology = rdf_generator.generate_rdf(classes, properties) -rdf_ontology = rdf_generator.generate_rdf_from_triples(triples) -rdf_ontology = rdf_generator.generate_rdf_with_namespaces(classes, properties, namespaces) - -# Turtle generation -turtle_generator = TurtleGenerator() -turtle_ontology = turtle_generator.generate_turtle(classes, properties) -turtle_ontology = turtle_generator.generate_turtle_from_triples(triples) -turtle_ontology = turtle_generator.generate_turtle_with_prefixes(classes, properties, prefixes) -``` - -**Base Mapper (`semantica.ontology.base_mapper`):** -```python -from semantica.ontology.base_mapper import SchemaMapper, FOAFMapper, DublinCoreMapper - -# Schema.org mapping -schema_mapper = SchemaMapper() -mapped = schema_mapper.map_to_schema_org(classes, properties) -mapped = schema_mapper.map_entities_to_schema_org(entities) -mapped = schema_mapper.map_relations_to_schema_org(relations) - -# FOAF mapping -foaf_mapper = FOAFMapper() -mapped = foaf_mapper.map_to_foaf(classes, properties) -mapped = foaf_mapper.map_persons_to_foaf(entities) -mapped = foaf_mapper.map_organizations_to_foaf(entities) - -# Dublin Core mapping -dc_mapper = DublinCoreMapper() -mapped = dc_mapper.map_to_dublin_core(classes, properties) -mapped = dc_mapper.map_documents_to_dublin_core(documents) -mapped = dc_mapper.map_metadata_to_dublin_core(metadata) -``` - -**Version Manager (`semantica.ontology.version_manager`):** -```python -from semantica.ontology.version_manager import VersionManager, MigrationManager, OntologyVersioner - -# Version management -version_manager = VersionManager() -version = version_manager.create_version(ontology, version="1.0.0") -version = version_manager.get_version(ontology, version="1.0.0") -versions = version_manager.list_versions(ontology) - -# Migration management -migration_manager = MigrationManager() -migrated = migration_manager.migrate_ontology(ontology, from_version="1.0.0", to_version="2.0.0") -migrated = migration_manager.migrate_triples(triples, migration_rules) -migrated = migration_manager.migrate_classes(classes, migration_rules) - -# Ontology versioning -ontology_versioner = OntologyVersioner() -versioned = ontology_versioner.version_ontology(ontology) -versioned = ontology_versioner.compare_versions(ontology_v1, ontology_v2) -versioned = ontology_versioner.merge_versions(ontology_v1, ontology_v2) -``` - -### 9. **Knowledge Graph** (`semantica.kg`) - -**Main Classes:** `GraphBuilder`, `EntityResolver`, `Deduplicator` - -**Purpose:** Build and manage knowledge graphs - -#### **Imports:** -```python -from semantica.kg import GraphBuilder, EntityResolver, Deduplicator -from semantica.kg.graph_builder import NodeBuilder, EdgeBuilder, SubgraphBuilder -from semantica.kg.entity_resolver import IdentityResolver, EntityMerger, EntityMatcher -from semantica.kg.deduplicator import DuplicateFinder, EntityMerger, SimilarityCalculator -from semantica.kg.graph_analyzer import GraphAnalyzer, PathFinder, CentralityCalculator -from semantica.kg.graph_optimizer import GraphOptimizer, IndexBuilder, QueryOptimizer -``` - -#### **Main Functions:** -```python -# Build knowledge graph -graph_builder = GraphBuilder() -node = graph_builder.create_node("Apple Inc.", "Company") -edge = graph_builder.create_edge("Apple Inc.", "founded_by", "Steve Jobs") -subgraph = graph_builder.build_subgraph(entities) -graph = graph_builder.build_complete_graph(triples) - -# Entity resolution -resolver = EntityResolver() -canonical = resolver.resolve_identity("Apple Inc.", "Apple") -merged = resolver.merge_entities(duplicate_entities) -resolved = resolver.resolve_all_entities(entities) - -# Deduplication -deduplicator = Deduplicator() -duplicates = deduplicator.find_duplicates(entities) -merged = deduplicator.merge_duplicates(duplicates) -cleaned = deduplicator.clean_graph(graph) -``` - -#### **Submodules with Functions:** - -**Graph Builder (`semantica.kg.graph_builder`):** -```python -from semantica.kg.graph_builder import NodeBuilder, EdgeBuilder, SubgraphBuilder - -# Node building -node_builder = NodeBuilder() -node = node_builder.create_node("Apple Inc.", "Company", properties={"founded": 1976}) -node = node_builder.create_node_with_id("Apple Inc.", "Company", node_id="apple_inc") -nodes = node_builder.create_nodes_batch(entities) - -# Edge building -edge_builder = EdgeBuilder() -edge = edge_builder.create_edge("Apple Inc.", "founded_by", "Steve Jobs") -edge = edge_builder.create_edge_with_properties("Apple Inc.", "founded_by", "Steve Jobs", {"year": 1976}) -edges = edge_builder.create_edges_batch(relations) - -# Subgraph building -subgraph_builder = SubgraphBuilder() -subgraph = subgraph_builder.build_subgraph(entities, relations) -subgraph = subgraph_builder.build_subgraph_by_type(entities, entity_type="Company") -subgraph = subgraph_builder.build_subgraph_by_relation(entities, relation_type="founded_by") -``` - -**Entity Resolver (`semantica.kg.entity_resolver`):** -```python -from semantica.kg.entity_resolver import IdentityResolver, EntityMerger, EntityMatcher - -# Identity resolution -identity_resolver = IdentityResolver() -canonical = identity_resolver.resolve_identity("Apple Inc.", "Apple") -canonical = identity_resolver.resolve_identity_batch(entities) -canonical = identity_resolver.resolve_identity_with_confidence("Apple Inc.", "Apple", threshold=0.8) - -# Entity merging -entity_merger = EntityMerger() -merged = entity_merger.merge_entities(duplicate_entities) -merged = entity_merger.merge_entities_with_strategy(duplicate_entities, strategy="highest_confidence") -merged = entity_merger.merge_entities_with_validation(duplicate_entities, validation_rules) - -# Entity matching -entity_matcher = EntityMatcher() -matches = entity_matcher.find_matches("Apple Inc.", entities) -matches = entity_matcher.find_matches_with_similarity("Apple Inc.", entities, threshold=0.8) -matches = entity_matcher.find_matches_with_fuzzy("Apple Inc.", entities, fuzzy_threshold=0.7) -``` - -**Deduplicator (`semantica.kg.deduplicator`):** -```python -from semantica.kg.deduplicator import DuplicateFinder, EntityMerger, SimilarityCalculator - -# Duplicate finding -duplicate_finder = DuplicateFinder() -duplicates = duplicate_finder.find_duplicates(entities) -duplicates = duplicate_finder.find_duplicates_by_similarity(entities, threshold=0.8) -duplicates = duplicate_finder.find_duplicates_by_fuzzy_matching(entities, fuzzy_threshold=0.7) - -# Entity merging -entity_merger = EntityMerger() -merged = entity_merger.merge_duplicates(duplicates) -merged = entity_merger.merge_duplicates_with_strategy(duplicates, strategy="merge_properties") -merged = entity_merger.merge_duplicates_with_validation(duplicates, validation_rules) - -# Similarity calculation -similarity_calculator = SimilarityCalculator() -similarity = similarity_calculator.calculate_similarity("Apple Inc.", "Apple") -similarity = similarity_calculator.calculate_similarity_batch(entities) -similarity = similarity_calculator.calculate_similarity_with_weights(entities, weights) -``` - -**Graph Analyzer (`semantica.kg.graph_analyzer`):** -```python -from semantica.kg.graph_analyzer import GraphAnalyzer, PathFinder, CentralityCalculator - -# Graph analysis -graph_analyzer = GraphAnalyzer() -stats = graph_analyzer.analyze_graph(graph) -stats = graph_analyzer.analyze_connectivity(graph) -stats = graph_analyzer.analyze_centrality(graph) - -# Path finding -path_finder = PathFinder() -paths = path_finder.find_paths("Apple Inc.", "Steve Jobs", graph) -paths = path_finder.find_shortest_paths("Apple Inc.", "Steve Jobs", graph) -paths = path_finder.find_all_paths("Apple Inc.", "Steve Jobs", graph, max_length=3) - -# Centrality calculation -centrality_calculator = CentralityCalculator() -centrality = centrality_calculator.calculate_centrality(graph) -centrality = centrality_calculator.calculate_betweenness_centrality(graph) -centrality = centrality_calculator.calculate_eigenvector_centrality(graph) -``` - -**Graph Optimizer (`semantica.kg.graph_optimizer`):** -```python -from semantica.kg.graph_optimizer import GraphOptimizer, IndexBuilder, QueryOptimizer - -# Graph optimization -graph_optimizer = GraphOptimizer() -optimized = graph_optimizer.optimize_graph(graph) -optimized = graph_optimizer.optimize_for_queries(graph, query_patterns) -optimized = graph_optimizer.optimize_for_storage(graph) - -# Index building -index_builder = IndexBuilder() -index = index_builder.build_index(graph) -index = index_builder.build_property_index(graph, properties=["name", "type"]) -index = index_builder.build_relationship_index(graph, relationships=["founded_by"]) - -# Query optimization -query_optimizer = QueryOptimizer() -optimized_query = query_optimizer.optimize_query(query, graph) -optimized_query = query_optimizer.optimize_query_with_index(query, graph, index) -optimized_query = query_optimizer.optimize_query_with_statistics(query, graph, statistics) -``` - -**Submodules:** -- `graph_builder` - Knowledge graph construction -- `entity_resolver` - Entity disambiguation and merging -- `deduplicator` - Duplicate detection and resolution -- `seed_manager` - Initial data loading -- `provenance_tracker` - Source tracking and confidence -- `conflict_detector` - Conflict identification and resolution - ---- - -## 💾 Storage & Retrieval - -### 10. **Vector Store** (`semantica.vector_store`) - -**Main Classes:** `PineconeAdapter`, `FAISSAdapter`, `WeaviateAdapter` - -**Purpose:** Store and search vector embeddings - -#### **Imports:** -```python -from semantica.vector_store import PineconeAdapter, FAISSAdapter, WeaviateAdapter -from semantica.vector_store.pinecone_adapter import PineconeIndex, PineconeQuery, PineconeMetadata -from semantica.vector_store.faiss_adapter import FAISSIndex, FAISSSearch, FAISSIndexBuilder -from semantica.vector_store.milvus_adapter import MilvusClient, MilvusCollection, MilvusSearch -from semantica.vector_store.weaviate_adapter import WeaviateClient, WeaviateSchema, WeaviateQuery -from semantica.vector_store.qdrant_adapter import QdrantClient, QdrantCollection, QdrantSearch -from semantica.vector_store.hybrid_search import HybridSearch, MetadataFilter, SearchRanker -``` - -#### **Main Functions:** -```python -# Pinecone integration -pinecone = PineconeAdapter() -pinecone.connect(api_key="your-key") -pinecone.create_index("semantica-index", dimension=1536) -pinecone.upsert_vectors(vectors, metadata) -results = pinecone.query_vectors(query_vector, top_k=10) - -# FAISS integration -faiss = FAISSAdapter() -faiss.create_index("IVFFlat", dimension=1536) -faiss.add_vectors(vectors) -faiss.save_index("index.faiss") -similar = faiss.search_similar(query_vector, k=10) - -# Weaviate integration -weaviate = WeaviateAdapter() -weaviate.connect(url="http://localhost:8080") -weaviate.create_schema(schema_definition) -weaviate.insert_objects(objects) -results = weaviate.query_objects(query, class_name="Document") -``` - -#### **Submodules with Functions:** - -**Pinecone Adapter (`semantica.vector_store.pinecone_adapter`):** -```python -from semantica.vector_store.pinecone_adapter import PineconeIndex, PineconeQuery, PineconeMetadata - -# Index management -pinecone_index = PineconeIndex() -pinecone_index.create_index("semantica-index", dimension=1536, metric="cosine") -pinecone_index.describe_index("semantica-index") -pinecone_index.delete_index("semantica-index") - -# Vector operations -pinecone_index.upsert_vectors(vectors, metadata, namespace="documents") -pinecone_index.update_vectors(vectors, metadata) -pinecone_index.delete_vectors(ids) - -# Query operations -pinecone_query = PineconeQuery() -results = pinecone_query.query_vectors(query_vector, top_k=10, include_metadata=True) -results = pinecone_query.query_with_filter(query_vector, filter={"category": "technology"}) -results = pinecone_query.query_by_id(vector_id) - -# Metadata operations -pinecone_metadata = PineconeMetadata() -metadata = pinecone_metadata.create_metadata({"text": "sample", "category": "tech"}) -metadata = pinecone_metadata.validate_metadata(metadata) -metadata = pinecone_metadata.filter_metadata(metadata, {"category": "technology"}) -``` - -**FAISS Adapter (`semantica.vector_store.faiss_adapter`):** -```python -from semantica.vector_store.faiss_adapter import FAISSIndex, FAISSSearch, FAISSIndexBuilder - -# Index building -index_builder = FAISSIndexBuilder() -index = index_builder.build_index("IVFFlat", dimension=1536, nlist=100) -index = index_builder.build_index("HNSW", dimension=1536, M=16) -index = index_builder.build_index("PQ", dimension=1536, m=64) - -# Index operations -faiss_index = FAISSIndex() -faiss_index.add_vectors(vectors) -faiss_index.add_vectors_with_ids(vectors, ids) -faiss_index.remove_vectors(ids) -faiss_index.save_index("index.faiss") -faiss_index.load_index("index.faiss") - -# Search operations -faiss_search = FAISSSearch() -results = faiss_search.search_similar(query_vector, k=10) -results = faiss_search.search_with_ids(query_vector, ids, k=10) -results = faiss_search.search_range(query_vector, radius=0.5) -``` - -**Weaviate Adapter (`semantica.vector_store.weaviate_adapter`):** -```python -from semantica.vector_store.weaviate_adapter import WeaviateClient, WeaviateSchema, WeaviateQuery - -# Client operations -weaviate_client = WeaviateClient() -weaviate_client.connect(url="http://localhost:8080") -weaviate_client.get_cluster_info() -weaviate_client.get_schema() - -# Schema operations -weaviate_schema = WeaviateSchema() -weaviate_schema.create_class(class_definition) -weaviate_schema.update_class(class_name, class_definition) -weaviate_schema.delete_class(class_name) - -# Query operations -weaviate_query = WeaviateQuery() -results = weaviate_query.query_objects(query, class_name="Document") -results = weaviate_query.query_with_filters(query, filters={"category": "technology"}) -results = weaviate_query.query_similar(query_vector, class_name="Document", limit=10) -``` - -**Hybrid Search (`semantica.vector_store.hybrid_search`):** -```python -from semantica.vector_store.hybrid_search import HybridSearch, MetadataFilter, SearchRanker - -# Hybrid search -hybrid_search = HybridSearch() -results = hybrid_search.search(query, vector_weight=0.7, metadata_weight=0.3) -results = hybrid_search.search_with_filters(query, filters={"category": "technology"}) -results = hybrid_search.search_with_reranking(query, rerank_method="diversity") - -# Metadata filtering -metadata_filter = MetadataFilter() -filtered = metadata_filter.filter_by_metadata(results, {"category": "technology"}) -filtered = metadata_filter.filter_by_range(results, "date", start="2023-01-01", end="2023-12-31") -filtered = metadata_filter.filter_by_boolean(results, {"is_published": True}) - -# Search ranking -search_ranker = SearchRanker() -ranked = search_ranker.rank_by_relevance(results, query) -ranked = search_ranker.rank_by_diversity(results, diversity_threshold=0.8) -ranked = search_ranker.rank_by_hybrid_score(results, vector_score=0.6, metadata_score=0.4) -``` - -### 11. **Triple Store** (`semantica.triple_store`) - -**Main Classes:** `BlazegraphAdapter`, `JenaAdapter`, `GraphDBAdapter` - -**Purpose:** Store and query RDF triples - -```python -from semantica.triple_store import BlazegraphAdapter, JenaAdapter - -# Blazegraph integration -blazegraph = BlazegraphAdapter() -blazegraph.connect("http://localhost:9999/blazegraph") -blazegraph.bulk_load(triples) - -# SPARQL queries -sparql_query = """ -SELECT ?subject ?predicate ?object -WHERE { ?subject ?predicate ?object } -LIMIT 10 -""" -results = blazegraph.execute_sparql(sparql_query) - -# Jena integration -jena = JenaAdapter() -model = jena.create_model() -jena.add_triples(model, triples) -inferred = jena.run_inference(model) -``` - -**Submodules:** -- `blazegraph_adapter` - Blazegraph SPARQL endpoint -- `jena_adapter` - Apache Jena RDF framework -- `rdf4j_adapter` - Eclipse RDF4J -- `graphdb_adapter` - GraphDB with reasoning -- `virtuoso_adapter` - Virtuoso RDF store - -### 12. **Embeddings** (`semantica.embeddings`) - -**Main Class:** `SemanticEmbedder` - -**Purpose:** Generate semantic embeddings for text and multimodal content - -```python -from semantica.embeddings import SemanticEmbedder - -# Initialize embedder -embedder = SemanticEmbedder( - model="text-embedding-3-large", - dimension=1536, - preserve_context=True -) - -# Generate embeddings -text_embeddings = embedder.embed_text("Hello world") -sentence_embeddings = embedder.embed_sentence("This is a sentence") -document_embeddings = embedder.embed_document(long_document) - -# Batch processing -batch_embeddings = embedder.batch_process(texts) -stats = embedder.get_embedding_stats() -``` - -**Submodules:** -- `text_embedder` - Text-based embeddings -- `image_embedder` - Image embeddings and vision models -- `audio_embedder` - Audio embeddings and speech recognition -- `multimodal_embedder` - Cross-modal embeddings -- `context_manager` - Context window management -- `pooling_strategies` - Various pooling strategies - ---- - -## 🤖 AI & Reasoning - -### 13. **RAG System** (`semantica.qa_rag`) - -**Main Classes:** `RAGManager`, `SemanticChunker`, `AnswerBuilder` - -**Purpose:** Question answering and retrieval-augmented generation - -#### **Imports:** -```python -from semantica.qa_rag import RAGManager, SemanticChunker, AnswerBuilder -from semantica.qa_rag.semantic_chunker import RAGChunker, ContextChunker, OverlapChunker -from semantica.qa_rag.prompt_templates import PromptTemplate, ContextTemplate, AnswerTemplate -from semantica.qa_rag.retrieval_policies import RetrievalPolicy, RankingPolicy, FilterPolicy -from semantica.qa_rag.answer_builder import AnswerBuilder, AttributionBuilder, ConfidenceCalculator -from semantica.qa_rag.provenance_tracker import SourceTracker, ConfidenceTracker, AttributionTracker -from semantica.qa_rag.conversation_manager import ConversationManager, ContextManager, HistoryManager -``` - -#### **Main Functions:** -```python -# Initialize RAG system -rag = RAGManager( - retriever="semantic", - generator="gpt-4", - chunk_size=512, - overlap=50 -) - -# Process question -question = "What are the key features of Semantica?" -answer = rag.process_question(question) -sources = rag.get_sources() -confidence = rag.get_confidence() - -# Semantic chunking for RAG -chunker = SemanticChunker() -chunks = chunker.chunk_text(document, optimize_for_rag=True) -chunks = chunker.chunk_with_context(document, context_window=200) -``` - -#### **Submodules with Functions:** - -**Semantic Chunker (`semantica.qa_rag.semantic_chunker`):** -```python -from semantica.qa_rag.semantic_chunker import RAGChunker, ContextChunker, OverlapChunker - -# RAG-optimized chunking -rag_chunker = RAGChunker() -chunks = rag_chunker.chunk_for_rag(document, chunk_size=512) -chunks = rag_chunker.chunk_with_semantic_boundaries(document) -chunks = rag_chunker.chunk_with_entity_preservation(document) - -# Context-aware chunking -context_chunker = ContextChunker() -chunks = context_chunker.chunk_with_context(document, context_size=100) -chunks = context_chunker.chunk_with_overlap_context(document, overlap=50) -chunks = context_chunker.chunk_with_semantic_context(document) - -# Overlap chunking -overlap_chunker = OverlapChunker() -chunks = overlap_chunker.chunk_with_overlap(document, overlap_ratio=0.2) -chunks = overlap_chunker.chunk_with_sliding_window(document, window_size=512, step=256) -chunks = overlap_chunker.chunk_with_adaptive_overlap(document) -``` - -**Prompt Templates (`semantica.qa_rag.prompt_templates`):** -```python -from semantica.qa_rag.prompt_templates import PromptTemplate, ContextTemplate, AnswerTemplate - -# Prompt template management -prompt_template = PromptTemplate() -template = prompt_template.create_template("qa_template", question="{question}", context="{context}") -template = prompt_template.create_template("summarization", text="{text}", length="{length}") -template = prompt_template.create_template("classification", text="{text}", categories="{categories}") - -# Context templates -context_template = ContextTemplate() -context = context_template.format_context(retrieved_chunks, question) -context = context_template.format_context_with_metadata(retrieved_chunks, question, metadata) -context = context_template.format_context_with_ranking(retrieved_chunks, question, rankings) - -# Answer templates -answer_template = AnswerTemplate() -answer = answer_template.format_answer(response, sources) -answer = answer_template.format_answer_with_attribution(response, sources, attributions) -answer = answer_template.format_answer_with_confidence(response, sources, confidence_scores) -``` - -**Retrieval Policies (`semantica.qa_rag.retrieval_policies`):** -```python -from semantica.qa_rag.retrieval_policies import RetrievalPolicy, RankingPolicy, FilterPolicy - -# Retrieval policy management -retrieval_policy = RetrievalPolicy() -results = retrieval_policy.retrieve(query, top_k=10) -results = retrieval_policy.retrieve_with_filters(query, filters={"category": "technology"}) -results = retrieval_policy.retrieve_with_reranking(query, rerank_method="diversity") - -# Ranking policies -ranking_policy = RankingPolicy() -ranked = ranking_policy.rank_by_relevance(results, query) -ranked = ranking_policy.rank_by_diversity(results, diversity_threshold=0.8) -ranked = ranking_policy.rank_by_hybrid_score(results, vector_weight=0.7, metadata_weight=0.3) - -# Filter policies -filter_policy = FilterPolicy() -filtered = filter_policy.filter_by_metadata(results, {"category": "technology"}) -filtered = filter_policy.filter_by_date_range(results, start_date="2023-01-01", end_date="2023-12-31") -filtered = filter_policy.filter_by_confidence(results, min_confidence=0.8) -``` - -**Answer Builder (`semantica.qa_rag.answer_builder`):** -```python -from semantica.qa_rag.answer_builder import AnswerBuilder, AttributionBuilder, ConfidenceCalculator - -# Answer construction -answer_builder = AnswerBuilder() -answer = answer_builder.build_answer(query, retrieved_chunks, llm_response) -answer = answer_builder.build_answer_with_sources(query, retrieved_chunks, llm_response, sources) -answer = answer_builder.build_answer_with_confidence(query, retrieved_chunks, llm_response, confidence) - -# Attribution building -attribution_builder = AttributionBuilder() -attributions = attribution_builder.build_attributions(answer, sources) -attributions = attribution_builder.build_attributions_with_confidence(answer, sources, confidence_scores) -attributions = attribution_builder.build_attributions_with_metadata(answer, sources, metadata) - -# Confidence calculation -confidence_calculator = ConfidenceCalculator() -confidence = confidence_calculator.calculate_confidence(answer, sources) -confidence = confidence_calculator.calculate_confidence_with_llm(answer, sources, llm_confidence) -confidence = confidence_calculator.calculate_confidence_with_retrieval(answer, sources, retrieval_scores) -``` - -**Conversation Manager (`semantica.qa_rag.conversation_manager`):** -```python -from semantica.qa_rag.conversation_manager import ConversationManager, ContextManager, HistoryManager - -# Conversation management -conversation_manager = ConversationManager() -conversation = conversation_manager.start_conversation() -conversation = conversation_manager.add_turn(conversation, question, answer) -conversation = conversation_manager.get_conversation_history(conversation_id) - -# Context management -context_manager = ContextManager() -context = context_manager.build_context(conversation_history) -context = context_manager.build_context_with_entities(conversation_history, entities) -context = context_manager.build_context_with_topics(conversation_history, topics) - -# History management -history_manager = HistoryManager() -history = history_manager.save_conversation(conversation) -history = history_manager.load_conversation(conversation_id) -history = history_manager.search_conversations(query, filters={"user_id": "user123"}) -``` - -### 14. **Reasoning Engine** (`semantica.reasoning`) - -**Main Classes:** `InferenceEngine`, `SPARQLReasoner`, `AbductiveReasoner` - -**Purpose:** Logical reasoning and inference - -```python -from semantica.reasoning import InferenceEngine, SPARQLReasoner - -# Rule-based inference -inference = InferenceEngine() -inference.add_rule("IF ?x is_a Company AND ?x founded_by ?y THEN ?y is_a Person") -inference.forward_chain() -inference.backward_chain() - -# SPARQL reasoning -sparql_reasoner = SPARQLReasoner() -expanded_query = sparql_reasoner.expand_query(sparql_query) -inferred_results = sparql_reasoner.infer_results(query_results) -``` - -**Submodules:** -- `inference_engine` - Rule-based inference -- `sparql_reasoner` - SPARQL-based reasoning -- `rete_engine` - Rete algorithm implementation -- `abductive_reasoner` - Abductive reasoning -- `deductive_reasoner` - Deductive reasoning -- `explanation_generator` - Reasoning explanations - -### 15. **Multi-Agent System** (`semantica.agents`) - -**Main Classes:** `AgentManager`, `OrchestrationEngine`, `MultiAgentManager` - -**Purpose:** Multi-agent coordination and workflows - -```python -from semantica.agents import AgentManager, OrchestrationEngine - -# Agent management -agent_manager = AgentManager() -agent = agent_manager.register_agent("data_processor", capabilities=["parse", "extract"]) -agent_manager.start_agent(agent) - -# Multi-agent orchestration -orchestrator = OrchestrationEngine() -workflow = orchestrator.coordinate_agents([ - "ingestion_agent", - "parsing_agent", - "extraction_agent", - "embedding_agent" -]) -results = orchestrator.distribute_tasks(workflow, tasks) -``` - -**Submodules:** -- `agent_manager` - Agent lifecycle management -- `orchestration_engine` - Multi-agent coordination -- `tool_registry` - Tool registration and discovery -- `cost_tracker` - Cost monitoring and optimization -- `sandbox_manager` - Agent sandboxing and security -- `workflow_engine` - Workflow definition and execution - ---- - -## 🚀 Quick Start Examples - -### Complete Pipeline Example - -```python -from semantica import Semantica -from semantica.pipeline import PipelineBuilder - -# Initialize Semantica -core = Semantica( - llm_provider="openai", - embedding_model="text-embedding-3-large", - vector_store="pinecone", - graph_db="neo4j" -) - -# Build processing pipeline -pipeline = PipelineBuilder() \ - .add_step("ingest", {"source": "documents/", "formats": ["pdf", "docx"]}) \ - .add_step("parse", {"extract_tables": True, "extract_images": True}) \ - .add_step("normalize", {"clean_text": True, "detect_language": True}) \ - .add_step("chunk", {"strategy": "semantic", "size": 512}) \ - .add_step("extract", {"entities": True, "relations": True, "triples": True}) \ - .add_step("embed", {"model": "text-embedding-3-large"}) \ - .add_step("store", {"vector_store": "pinecone", "triple_store": "neo4j"}) \ - .set_parallelism(4) \ - .build() - -# Execute pipeline -results = pipeline.run() - -# Query results -knowledge_base = core.build_knowledge_base("documents/") -answer = knowledge_base.query("What are the main topics?") -``` - - ---- - -## 📚 Additional Resources - -- **Documentation**: [https://semantica.readthedocs.io/](https://semantica.readthedocs.io/) -- **API Reference**: [https://semantica.readthedocs.io/api/](https://semantica.readthedocs.io/api/) -- **Examples Repository**: [https://github.com/semantica/examples](https://github.com/semantica/examples) -- **Community**: [https://discord.gg/semantica](https://discord.gg/semantica) - ---- - -## 🔧 Knowledge Graph Quality Assurance - -> **Addressing the fundamental challenges in building production-ready Knowledge Graphs** - -### 16. **Template System** (`semantica.templates`) - -**Main Classes:** `SchemaTemplate`, `EntityTemplate`, `RelationshipTemplate` - -**Purpose:** Enforce fixed, predefined schemas for consistent and predictable Knowledge Graph structure - -#### **Imports:** -```python -from semantica.templates import SchemaTemplate, EntityTemplate, RelationshipTemplate -from semantica.templates.schema_manager import SchemaManager, SchemaValidator, SchemaEnforcer -from semantica.templates.entity_templates import EntityTemplateManager, EntityValidator, EntityEnforcer -from semantica.templates.relationship_templates import RelationshipTemplateManager, RelationshipValidator -from semantica.templates.template_loader import TemplateLoader, TemplateParser, TemplateCompiler -from semantica.templates.constraint_enforcer import ConstraintEnforcer, ValidationEngine, ComplianceChecker -``` - -#### **Main Functions:** -```python -# Define fixed schema template -schema_template = SchemaTemplate( - name="business_knowledge_graph", - entities=["Company", "Person", "Product", "Department", "Project"], - relationships=["founded_by", "works_for", "manages", "belongs_to", "reports_to"], - constraints={ - "Company": {"required_props": ["name", "industry", "founded_year"]}, - "Person": {"required_props": ["name", "title", "department"]}, - "founded_by": {"domain": "Company", "range": "Person"} - } -) - -# Enforce template compliance -schema_template.enforce_schema(extracted_entities) -schema_template.validate_relationships(extracted_relations) -schema_template.apply_constraints(knowledge_graph) -``` - -#### **Submodules with Functions:** - -**Schema Manager (`semantica.templates.schema_manager`):** -```python -from semantica.templates.schema_manager import SchemaManager, SchemaValidator, SchemaEnforcer - -# Schema management -schema_manager = SchemaManager() -schema = schema_manager.load_schema("business_schema.yaml") -schema = schema_manager.create_schema_from_template(template) -schema = schema_manager.update_schema(schema, updates) - -# Schema validation -schema_validator = SchemaValidator() -is_valid = schema_validator.validate_entities(entities, schema) -is_valid = schema_validator.validate_relationships(relationships, schema) -is_valid = schema_validator.validate_properties(properties, schema) - -# Schema enforcement -schema_enforcer = SchemaEnforcer() -enforced = schema_enforcer.enforce_entity_schema(entities, schema) -enforced = schema_enforcer.enforce_relationship_schema(relationships, schema) -enforced = schema_enforcer.enforce_property_schema(properties, schema) -``` - -**Entity Templates (`semantica.templates.entity_templates`):** -```python -from semantica.templates.entity_templates import EntityTemplateManager, EntityValidator, EntityEnforcer - -# Entity template management -entity_template_manager = EntityTemplateManager() -template = entity_template_manager.create_template("Company", required_props=["name", "industry"]) -template = entity_template_manager.load_template("person_template.yaml") -template = entity_template_manager.update_template(template, new_constraints) - -# Entity validation -entity_validator = EntityValidator() -is_valid = entity_validator.validate_entity(entity, template) -is_valid = entity_validator.validate_entity_batch(entities, template) -is_valid = entity_validator.validate_required_properties(entity, template) - -# Entity enforcement -entity_enforcer = EntityEnforcer() -enforced = entity_enforcer.enforce_template(entity, template) -enforced = entity_enforcer.add_missing_properties(entity, template) -enforced = entity_enforcer.normalize_entity(entity, template) -``` - -### 17. **Seed Data System** (`semantica.seed`) - -**Main Classes:** `SeedDataManager`, `KnowledgeSeeder`, `DataIntegrator` - -**Purpose:** Initialize Knowledge Graph with pre-existing, verified data to build on foundation of truth - -#### **Imports:** -```python -from semantica.seed import SeedDataManager, KnowledgeSeeder, DataIntegrator -from semantica.seed.data_loader import CSVLoader, JSONLoader, DatabaseLoader, APILoader -from semantica.seed.entity_seeder import EntitySeeder, RelationshipSeeder, PropertySeeder -from semantica.seed.verification import DataVerifier, ConsistencyChecker, TruthValidator -from semantica.seed.integration import DataIntegrator, ConflictResolver, MergeStrategy -``` - -#### **Main Functions:** -```python -# Initialize with seed data -seed_manager = SeedDataManager() -seed_manager.load_products("products.csv") -seed_manager.load_departments("departments.json") -seed_manager.load_employees("employees_db") - -# Seed knowledge graph -knowledge_seeder = KnowledgeSeeder() -seeded_graph = knowledge_seeder.seed_entities(seed_data) -seeded_graph = knowledge_seeder.seed_relationships(seed_data) -seeded_graph = knowledge_seeder.seed_properties(seed_data) - -# Integrate with extracted data -integrator = DataIntegrator() -integrated = integrator.integrate_seed_with_extracted(seed_data, extracted_data) -integrated = integrator.merge_verified_data(seed_data, extracted_data) -``` - -#### **Submodules with Functions:** - -**Data Loader (`semantica.seed.data_loader`):** -```python -from semantica.seed.data_loader import CSVLoader, JSONLoader, DatabaseLoader, APILoader - -# CSV data loading -csv_loader = CSVLoader() -products = csv_loader.load_entities("products.csv", entity_type="Product") -departments = csv_loader.load_entities("departments.csv", entity_type="Department") - -# JSON data loading -json_loader = JSONLoader() -employees = json_loader.load_entities("employees.json", entity_type="Person") -companies = json_loader.load_entities("companies.json", entity_type="Company") - -# Database loading -db_loader = DatabaseLoader(connection_string="postgresql://...") -customers = db_loader.load_entities("customers", entity_type="Person") -orders = db_loader.load_relationships("orders", relationship_type="placed_by") - -# API loading -api_loader = APILoader(api_key="your_key") -external_data = api_loader.load_from_api("https://api.example.com/entities") -``` - -**Entity Seeder (`semantica.seed.entity_seeder`):** -```python -from semantica.seed.entity_seeder import EntitySeeder, RelationshipSeeder, PropertySeeder - -# Entity seeding -entity_seeder = EntitySeeder() -seeded = entity_seeder.seed_entities(products, entity_type="Product") -seeded = entity_seeder.seed_with_verification(employees, verification_rules) -seeded = entity_seeder.seed_with_metadata(companies, metadata={"source": "verified"}) - -# Relationship seeding -relationship_seeder = RelationshipSeeder() -seeded = relationship_seeder.seed_relationships(org_chart, relationship_type="reports_to") -seeded = relationship_seeder.seed_with_hierarchy(relationships, hierarchy_rules) - -# Property seeding -property_seeder = PropertySeeder() -seeded = property_seeder.seed_properties(entities, property_mappings) -seeded = property_seeder.seed_with_validation(properties, validation_rules) -``` - -### 18. **Advanced Deduplication** (`semantica.deduplication`) - -**Main Classes:** `DuplicateDetector`, `EntityMerger`, `SimilarityEngine` - -**Purpose:** Identify and merge duplicate entities like "First Quarter Sales" and "Q1 Sales Report" - -#### **Imports:** -```python -from semantica.deduplication import DuplicateDetector, EntityMerger, SimilarityEngine -from semantica.deduplication.similarity_calculator import SemanticSimilarity, FuzzyMatcher, PhoneticMatcher -from semantica.deduplication.merge_strategies import MergeStrategy, PropertyMerger, RelationshipMerger -from semantica.deduplication.conflict_resolver import ConflictResolver, MergeConflictHandler, ResolutionStrategy -from semantica.deduplication.quality_assessor import QualityAssessor, MergeQualityChecker, ConfidenceCalculator -``` - -#### **Main Functions:** -```python -# Detect duplicates -duplicate_detector = DuplicateDetector() -duplicates = duplicate_detector.find_semantic_duplicates(entities) -duplicates = duplicate_detector.find_fuzzy_duplicates(entities, threshold=0.8) -duplicates = duplicate_detector.find_phonetic_duplicates(entities) - -# Merge duplicates -entity_merger = EntityMerger() -merged = entity_merger.merge_duplicates(duplicates) -merged = entity_merger.merge_with_strategy(duplicates, strategy="highest_confidence") -merged = entity_merger.merge_with_validation(duplicates, validation_rules) - -# Calculate similarity -similarity_engine = SimilarityEngine() -similarity = similarity_engine.calculate_semantic_similarity("First Quarter Sales", "Q1 Sales Report") -similarity = similarity_engine.calculate_fuzzy_similarity("Apple Inc.", "Apple Corporation") -similarity = similarity_engine.calculate_phonetic_similarity("Smith", "Smyth") -``` - -#### **Submodules with Functions:** - -**Similarity Calculator (`semantica.deduplication.similarity_calculator`):** -```python -from semantica.deduplication.similarity_calculator import SemanticSimilarity, FuzzyMatcher, PhoneticMatcher - -# Semantic similarity -semantic_similarity = SemanticSimilarity() -score = semantic_similarity.calculate("First Quarter Sales", "Q1 Sales Report") -score = semantic_similarity.calculate_batch(entity_pairs) -score = semantic_similarity.calculate_with_embeddings(entity1, entity2) - -# Fuzzy matching -fuzzy_matcher = FuzzyMatcher() -matches = fuzzy_matcher.find_matches("Apple Inc.", entities, threshold=0.8) -matches = fuzzy_matcher.find_partial_matches("Apple", entities, threshold=0.6) -matches = fuzzy_matcher.find_approximate_matches("Q1", entities, threshold=0.7) - -# Phonetic matching -phonetic_matcher = PhoneticMatcher() -matches = phonetic_matcher.find_phonetic_matches("Smith", entities) -matches = phonetic_matcher.find_soundex_matches("Johnson", entities) -matches = phonetic_matcher.find_metaphone_matches("Knight", entities) -``` - -**Merge Strategies (`semantica.deduplication.merge_strategies`):** -```python -from semantica.deduplication.merge_strategies import MergeStrategy, PropertyMerger, RelationshipMerger - -# Merge strategy management -merge_strategy = MergeStrategy() -merged = merge_strategy.merge_by_confidence(duplicates) -merged = merge_strategy.merge_by_completeness(duplicates) -merged = merge_strategy.merge_by_authority(duplicates, authority_sources) - -# Property merging -property_merger = PropertyMerger() -merged = property_merger.merge_properties(duplicate_entities) -merged = property_merger.merge_with_priority(duplicate_entities, priority_rules) -merged = property_merger.merge_with_validation(duplicate_entities, validation_rules) - -# Relationship merging -relationship_merger = RelationshipMerger() -merged = relationship_merger.merge_relationships(duplicate_entities) -merged = relationship_merger.merge_with_deduplication(duplicate_entities) -merged = relationship_merger.merge_with_consistency_check(duplicate_entities) -``` - -### 19. **Conflict Detection & Source Tracking** (`semantica.conflicts`) - -**Main Classes:** `ConflictDetector`, `SourceTracker`, `DisagreementResolver` - -**Purpose:** Flag when sources disagree and track exact document origins for investigation - -#### **Imports:** -```python -from semantica.conflicts import ConflictDetector, SourceTracker, DisagreementResolver -from semantica.conflicts.conflict_analyzer import ConflictAnalyzer, DisagreementDetector, InconsistencyFinder -from semantica.conflicts.source_tracker import SourceTracker, DocumentTracker, ProvenanceTracker -from semantica.conflicts.resolution_strategies import ResolutionStrategy, VotingResolver, AuthorityResolver -from semantica.conflicts.reporting import ConflictReporter, DisagreementReporter, InvestigationGuide -``` - -#### **Main Functions:** -```python -# Detect conflicts -conflict_detector = ConflictDetector() -conflicts = conflict_detector.detect_value_conflicts(entities, "sales_figure") -conflicts = conflict_detector.detect_property_conflicts(entities, "founded_year") -conflicts = conflict_detector.detect_relationship_conflicts(relationships) - -# Track sources -source_tracker = SourceTracker() -sources = source_tracker.track_entity_sources(entity, "Apple Inc.") -sources = source_tracker.track_property_sources(property, "sales_figure", "$10M") -sources = source_tracker.track_relationship_sources(relationship, "founded_by") - -# Resolve disagreements -disagreement_resolver = DisagreementResolver() -resolved = disagreement_resolver.resolve_by_voting(conflicts) -resolved = disagreement_resolver.resolve_by_authority(conflicts, authority_sources) -resolved = disagreement_resolver.flag_for_investigation(conflicts) -``` - -#### **Submodules with Functions:** - -**Conflict Analyzer (`semantica.conflicts.conflict_analyzer`):** -```python -from semantica.conflicts.conflict_analyzer import ConflictAnalyzer, DisagreementDetector, InconsistencyFinder - -# Conflict analysis -conflict_analyzer = ConflictAnalyzer() -conflicts = conflict_analyzer.analyze_value_conflicts(entities, property_name="sales_figure") -conflicts = conflict_analyzer.analyze_type_conflicts(entities, property_name="founded_year") -conflicts = conflict_analyzer.analyze_relationship_conflicts(relationships) - -# Disagreement detection -disagreement_detector = DisagreementDetector() -disagreements = disagreement_detector.detect_value_disagreements(entities, "sales_figure") -disagreements = disagreement_detector.detect_factual_disagreements(entities, "founded_year") -disagreements = disagreement_detector.detect_categorical_disagreements(entities, "industry") - -# Inconsistency finding -inconsistency_finder = InconsistencyFinder() -inconsistencies = inconsistency_finder.find_logical_inconsistencies(knowledge_graph) -inconsistencies = inconsistency_finder.find_temporal_inconsistencies(entities, "founded_year") -inconsistencies = inconsistency_finder.find_hierarchical_inconsistencies(relationships) -``` - -**Source Tracker (`semantica.conflicts.source_tracker`):** -```python -from semantica.conflicts.source_tracker import SourceTracker, DocumentTracker, ProvenanceTracker - -# Source tracking -source_tracker = SourceTracker() -sources = source_tracker.track_entity_sources(entity, "Apple Inc.") -sources = source_tracker.track_property_sources(property, "sales_figure", "$10M") -sources = source_tracker.track_relationship_sources(relationship, "founded_by") - -# Document tracking -document_tracker = DocumentTracker() -documents = document_tracker.get_source_documents(entity, "Apple Inc.") -documents = document_tracker.get_document_sections(entity, "Apple Inc.", "sales_figure") -documents = document_tracker.get_document_context(entity, "Apple Inc.", context_size=200) - -# Provenance tracking -provenance_tracker = ProvenanceTracker() -provenance = provenance_tracker.get_entity_provenance(entity, "Apple Inc.") -provenance = provenance_tracker.get_property_provenance(property, "sales_figure") -provenance = provenance_tracker.get_relationship_provenance(relationship, "founded_by") -``` - -**Conflict Reporter (`semantica.conflicts.reporting`):** -```python -from semantica.conflicts.reporting import ConflictReporter, DisagreementReporter, InvestigationGuide - -# Conflict reporting -conflict_reporter = ConflictReporter() -report = conflict_reporter.generate_conflict_report(conflicts) -report = conflict_reporter.generate_detailed_report(conflicts, include_sources=True) -report = conflict_reporter.generate_summary_report(conflicts) - -# Disagreement reporting -disagreement_reporter = DisagreementReporter() -report = disagreement_reporter.report_value_disagreements(disagreements, "sales_figure") -report = disagreement_reporter.report_factual_disagreements(disagreements, "founded_year") -report = disagreement_reporter.report_categorical_disagreements(disagreements, "industry") - -# Investigation guide -investigation_guide = InvestigationGuide() -guide = investigation_guide.create_investigation_plan(conflicts) -guide = investigation_guide.suggest_investigation_steps(conflicts) -guide = investigation_guide.prioritize_investigations(conflicts, priority_criteria) -``` - -### 20. **Knowledge Graph Quality Assurance** (`semantica.kg_qa`) - -**Main Classes:** `KGQualityAssessor`, `ConsistencyChecker`, `CompletenessValidator` - -**Purpose:** Comprehensive quality assurance for production-ready Knowledge Graphs - -#### **Imports:** -```python -from semantica.kg_qa import KGQualityAssessor, ConsistencyChecker, CompletenessValidator -from semantica.kg_qa.quality_metrics import QualityMetrics, CompletenessMetrics, ConsistencyMetrics -from semantica.kg_qa.validation_engine import ValidationEngine, RuleValidator, ConstraintValidator -from semantica.kg_qa.reporting import QualityReporter, IssueTracker, ImprovementSuggestions -from semantica.kg_qa.automated_fixes import AutomatedFixer, AutoMerger, AutoResolver -``` - -#### **Main Functions:** -```python -# Assess knowledge graph quality -kg_qa = KGQualityAssessor() -quality_score = kg_qa.assess_overall_quality(knowledge_graph) -quality_report = kg_qa.generate_quality_report(knowledge_graph) -quality_issues = kg_qa.identify_quality_issues(knowledge_graph) - -# Check consistency -consistency_checker = ConsistencyChecker() -is_consistent = consistency_checker.check_logical_consistency(knowledge_graph) -is_consistent = consistency_checker.check_temporal_consistency(knowledge_graph) -is_consistent = consistency_checker.check_hierarchical_consistency(knowledge_graph) - -# Validate completeness -completeness_validator = CompletenessValidator() -is_complete = completeness_validator.validate_entity_completeness(entities, schema) -is_complete = completeness_validator.validate_relationship_completeness(relationships, schema) -is_complete = completeness_validator.validate_property_completeness(properties, schema) -``` - -#### **Submodules with Functions:** - -**Quality Metrics (`semantica.kg_qa.quality_metrics`):** -```python -from semantica.kg_qa.quality_metrics import QualityMetrics, CompletenessMetrics, ConsistencyMetrics - -# Quality metrics calculation -quality_metrics = QualityMetrics() -score = quality_metrics.calculate_overall_score(knowledge_graph) -score = quality_metrics.calculate_entity_quality(entities) -score = quality_metrics.calculate_relationship_quality(relationships) - -# Completeness metrics -completeness_metrics = CompletenessMetrics() -score = completeness_metrics.calculate_entity_completeness(entities, schema) -score = completeness_metrics.calculate_property_completeness(properties, schema) -score = completeness_metrics.calculate_relationship_completeness(relationships, schema) - -# Consistency metrics -consistency_metrics = ConsistencyMetrics() -score = consistency_metrics.calculate_logical_consistency(knowledge_graph) -score = consistency_metrics.calculate_temporal_consistency(knowledge_graph) -score = consistency_metrics.calculate_hierarchical_consistency(knowledge_graph) -``` - -**Automated Fixes (`semantica.kg_qa.automated_fixes`):** -```python -from semantica.kg_qa.automated_fixes import AutomatedFixer, AutoMerger, AutoResolver - -# Automated fixing -automated_fixer = AutomatedFixer() -fixed = automated_fixer.fix_duplicates(knowledge_graph) -fixed = automated_fixer.fix_inconsistencies(knowledge_graph) -fixed = automated_fixer.fix_missing_properties(knowledge_graph) - -# Auto merging -auto_merger = AutoMerger() -merged = auto_merger.merge_duplicate_entities(knowledge_graph) -merged = auto_merger.merge_duplicate_relationships(knowledge_graph) -merged = auto_merger.merge_conflicting_properties(knowledge_graph) - -# Auto resolving -auto_resolver = AutoResolver() -resolved = auto_resolver.resolve_conflicts(knowledge_graph) -resolved = auto_resolver.resolve_disagreements(knowledge_graph) -resolved = auto_resolver.resolve_inconsistencies(knowledge_graph) -``` - ---- - -## 📚 Complete Module Index - -### All 20 Main Modules with Submodules - -| # | Module | Package | Main Classes | Submodules Count | -|---|--------|---------|--------------|------------------| -| 1 | **Core Engine** | `semantica.core` | `Semantica`, `Config`, `PluginManager` | 4 | -| 2 | **Pipeline Builder** | `semantica.pipeline` | `PipelineBuilder`, `ExecutionEngine` | 7 | -| 3 | **Data Ingestion** | `semantica.ingest` | `FileIngestor`, `WebIngestor`, `FeedIngestor` | 7 | -| 4 | **Document Parsing** | `semantica.parse` | `PDFParser`, `DOCXParser`, `HTMLParser` | 9 | -| 5 | **Text Normalization** | `semantica.normalize` | `TextCleaner`, `LanguageDetector` | 6 | -| 6 | **Text Chunking** | `semantica.split` | `SemanticChunker`, `StructuralChunker` | 5 | -| 7 | **Semantic Extraction** | `semantica.semantic_extract` | `NERExtractor`, `RelationExtractor` | 6 | -| 8 | **Ontology Generation** | `semantica.ontology` | `OntologyGenerator`, `ClassInferrer` | 6 | -| 9 | **Knowledge Graph** | `semantica.kg` | `GraphBuilder`, `EntityResolver` | 7 | -| 10 | **Vector Store** | `semantica.vector_store` | `PineconeAdapter`, `FAISSAdapter` | 6 | -| 11 | **Triple Store** | `semantica.triple_store` | `BlazegraphAdapter`, `JenaAdapter` | 5 | -| 12 | **Embeddings** | `semantica.embeddings` | `SemanticEmbedder`, `TextEmbedder` | 6 | -| 13 | **RAG System** | `semantica.qa_rag` | `RAGManager`, `SemanticChunker` | 7 | -| 14 | **Reasoning Engine** | `semantica.reasoning` | `InferenceEngine`, `SPARQLReasoner` | 7 | -| 15 | **Multi-Agent System** | `semantica.agents` | `AgentManager`, `OrchestrationEngine` | 8 | -| 16 | **Template System** | `semantica.templates` | `SchemaTemplate`, `EntityTemplate` | 5 | -| 17 | **Seed Data System** | `semantica.seed` | `SeedDataManager`, `KnowledgeSeeder` | 4 | -| 18 | **Advanced Deduplication** | `semantica.deduplication` | `DuplicateDetector`, `EntityMerger` | 5 | -| 19 | **Conflict Detection** | `semantica.conflicts` | `ConflictDetector`, `SourceTracker` | 4 | -| 20 | **KG Quality Assurance** | `semantica.kg_qa` | `KGQualityAssessor`, `ConsistencyChecker` | 5 | - -**Total: 20 Main Modules, 120+ Submodules** - ---- - -## 🔧 Complete Functions Reference - -### Module-by-Module Function Tables - -#### 1. **Core Engine Functions** (`semantica.core`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `Semantica.initialize()` | core | Setup all modules and connections | None | Status | -| `Semantica.build_knowledge_base()` | core | Process data sources into knowledge base | sources: List[str] | KnowledgeBase | -| `Semantica.get_status()` | core | Get system health and metrics | None | Dict | -| `Semantica.create_pipeline()` | core | Create processing pipeline | config: Dict | Pipeline | -| `Semantica.get_config()` | core | Get current configuration | None | Config | -| `Semantica.list_plugins()` | core | List available plugins | None | List[Plugin] | -| `Config.validate()` | config_manager | Validate configuration against schema | schema: str | bool | -| `PluginManager.load_plugin()` | plugin_registry | Dynamically load plugin modules | name: str, version: str | Plugin | -| `PluginManager.list_plugins()` | plugin_registry | Show available plugins and versions | None | List[Plugin] | -| `Orchestrator.schedule_pipeline()` | orchestrator | Schedule pipeline execution | pipeline_config: Dict | PipelineID | -| `Orchestrator.monitor_progress()` | orchestrator | Monitor pipeline progress | pipeline_id: str | Progress | -| `LifecycleManager.startup()` | lifecycle | Execute startup hooks | None | Status | -| `LifecycleManager.shutdown()` | lifecycle | Execute shutdown hooks | None | Status | - -#### 2. **Pipeline Builder Functions** (`semantica.pipeline`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `PipelineBuilder.add_step()` | pipeline | Add processing step to pipeline | name: str, config: Dict | PipelineBuilder | -| `PipelineBuilder.set_parallelism()` | pipeline | Configure parallel execution | level: int | PipelineBuilder | -| `PipelineBuilder.build()` | pipeline | Build the pipeline | None | Pipeline | -| `Pipeline.run()` | execution_engine | Execute complete pipeline | None | Results | -| `Pipeline.pause()` | execution_engine | Pause pipeline execution | None | Status | -| `Pipeline.resume()` | execution_engine | Resume paused pipeline | None | Status | -| `Pipeline.stop()` | execution_engine | Stop pipeline execution | None | Status | -| `ExecutionEngine.execute_pipeline()` | execution_engine | Execute pipeline with config | config: Dict | Results | -| `FailureHandler.retry_step()` | failure_handler | Retry failed step | step_id: str, error: Exception | Status | -| `FailureHandler.handle_error()` | failure_handler | Handle execution errors | error: Exception | RecoveryPlan | -| `ParallelExecutor.execute_parallel()` | parallelism_manager | Execute tasks in parallel | tasks: List[Task] | Results | -| `ResourceScheduler.allocate_cpu()` | resource_scheduler | Allocate CPU resources | cores: int | ResourceID | -| `ResourceScheduler.allocate_gpu()` | resource_scheduler | Allocate GPU resources | device_id: int | ResourceID | -| `PipelineValidator.validate_pipeline()` | pipeline_validator | Validate pipeline configuration | config: Dict | ValidationResult | - -#### 3. **Data Ingestion Functions** (`semantica.ingest`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `FileIngestor.scan_directory()` | file | Recursively scan directory for files | path: str, recursive: bool | List[File] | -| `FileIngestor.detect_format()` | file | Auto-detect file type and encoding | file_path: str | FileFormat | -| `WebIngestor.crawl_site()` | web | Crawl website with depth and rate limiting | url: str, max_depth: int | WebContent | -| `WebIngestor.extract_links()` | web | Extract and follow hyperlinks | content: WebContent | List[Link] | -| `FeedIngestor.parse_rss()` | feed | Parse RSS/Atom feeds with metadata | feed_url: str | FeedData | -| `StreamIngestor.connect()` | stream | Establish real-time data connection | config: Dict | StreamConnection | -| `RepoIngestor.clone_repo()` | repo | Clone and track repository changes | repo_url: str | Repository | -| `EmailIngestor.connect_imap()` | email | Connect to email server | server: str, credentials: Dict | EmailConnection | -| `DBIngestor.export_table()` | db_export | Export database table to structured format | table: str, query: str | StructuredData | -| `IngestManager.resume_from_token()` | ingest | Resume interrupted ingestion | token: str | Status | -| `IngestManager.get_progress()` | ingest | Monitor ingestion progress | None | Progress | -| `ConnectorRegistry.register()` | ingest | Register custom data connectors | connector: Connector | Status | - -#### 4. **Document Parsing Functions** (`semantica.parse`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `PDFParser.extract_text()` | pdf | Extract text with positioning and formatting | file_path: str | TextContent | -| `PDFParser.extract_tables()` | pdf | Extract tables using Camelot/Tabula | file_path: str | List[Table] | -| `PDFParser.extract_images()` | pdf | Extract embedded images and figures | file_path: str | List[Image] | -| `DOCXParser.get_document_structure()` | docx | Extract document outline and sections | file_path: str | DocumentStructure | -| `DOCXParser.extract_track_changes()` | docx | Extract revision history | file_path: str | TrackChanges | -| `PPTXParser.extract_slides()` | pptx | Extract slide content and speaker notes | file_path: str | List[Slide] | -| `ExcelParser.read_sheet()` | excel | Read specific worksheet with data types | file_path: str, sheet: str | Worksheet | -| `ExcelParser.extract_charts()` | excel | Extract chart data and metadata | file_path: str | List[Chart] | -| `HTMLParser.parse_dom()` | html | Parse HTML into structured DOM tree | url: str | DOMTree | -| `HTMLParser.extract_metadata()` | html | Extract meta tags and structured data | dom: DOMTree | Metadata | -| `ImageParser.ocr_text()` | images | Perform OCR using Tesseract/Google Vision | image_path: str | TextContent | -| `ImageParser.detect_objects()` | images | Detect objects and faces in images | image_path: str | List[Object] | -| `TableParser.detect_structure()` | tables | Detect table boundaries and headers | content: str | TableStructure | -| `TableParser.extract_cells()` | tables | Extract individual cell data | table: Table | List[Cell] | -| `ParserRegistry.get_parser()` | parse | Get appropriate parser for file type | file_type: str | Parser | -| `ParserRegistry.supported_formats()` | parse | List all supported file formats | None | List[str] | - -#### 5. **Text Normalization Functions** (`semantica.normalize`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `TextCleaner.remove_html()` | text_cleaner | Strip HTML tags and preserve text content | html: str | str | -| `TextCleaner.normalize_whitespace()` | text_cleaner | Standardize spacing and line breaks | text: str | str | -| `TextCleaner.remove_special_chars()` | text_cleaner | Clean special characters and symbols | text: str | str | -| `LanguageDetector.detect()` | language_detector | Identify text language with confidence score | text: str | Language | -| `LanguageDetector.supported_languages()` | language_detector | List all supported languages | None | List[str] | -| `EncodingHandler.normalize()` | encoding_handler | Convert to UTF-8 and validate encoding | text: bytes | str | -| `EncodingHandler.detect_encoding()` | encoding_handler | Auto-detect file encoding | file_path: str | str | -| `EntityNormalizer.canonicalize()` | entity_normalizer | Standardize entity names and aliases | entity: str, alias: str | str | -| `EntityNormalizer.expand_acronyms()` | entity_normalizer | Expand abbreviations and acronyms | text: str | str | -| `DateNormalizer.parse_date()` | date_normalizer | Parse various date formats to ISO standard | date_str: str | datetime | -| `DateNormalizer.resolve_relative()` | date_normalizer | Convert relative dates to absolute | date_str: str | datetime | -| `NumberNormalizer.standardize()` | number_normalizer | Convert numbers to standard format | number: str | str | -| `NumberNormalizer.convert_units()` | number_normalizer | Convert between measurement units | value: float, from_unit: str, to_unit: str | float | -| `NormalizationPipeline.run()` | normalize | Execute complete normalization pipeline | text: str | NormalizedText | -| `NormalizationPipeline.get_stats()` | normalize | Return normalization statistics | None | Dict | - -#### 6. **Text Chunking Functions** (`semantica.split`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `SlidingWindowChunker.split()` | sliding_window | Create fixed-size chunks with overlap | text: str, size: int, overlap: int | List[Chunk] | -| `SlidingWindowChunker.set_window_size()` | sliding_window | Configure chunk size and overlap | size: int, overlap: int | None | -| `SemanticChunker.split_by_meaning()` | semantic_chunker | Split based on semantic boundaries | text: str | List[Chunk] | -| `SemanticChunker.detect_topics()` | semantic_chunker | Identify topic changes for splitting | text: str | List[Topic] | -| `StructuralChunker.split_by_sections()` | structural_chunker | Split on document structure | document: Document | List[Chunk] | -| `StructuralChunker.identify_headers()` | structural_chunker | Detect section headers and levels | document: Document | List[Header] | -| `TableChunker.preserve_tables()` | table_chunker | Keep tables intact during splitting | document: Document | List[Chunk] | -| `TableChunker.extract_table_context()` | table_chunker | Extract surrounding context for tables | table: Table | str | -| `ProvenanceTracker.track_source()` | provenance_tracker | Track original source and position | chunk: Chunk | Provenance | -| `ProvenanceTracker.get_provenance()` | provenance_tracker | Retrieve chunk source information | chunk_id: str | Provenance | -| `ChunkValidator.validate_chunk()` | chunk_validator | Validate chunk quality and size | chunk: Chunk | ValidationResult | -| `ChunkValidator.detect_overlaps()` | chunk_validator | Find overlapping chunks | chunks: List[Chunk] | List[Overlap] | -| `SplitManager.run_strategy()` | split | Execute chosen splitting strategy | text: str, strategy: str | List[Chunk] | -| `SplitManager.get_chunk_stats()` | split | Return chunking statistics | None | Dict | - -#### 7. **Semantic Extraction Functions** (`semantica.semantic_extract`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `NERExtractor.extract_entities()` | ner_extractor | Extract named entities with types and confidence | text: str | List[Entity] | -| `NERExtractor.classify_entities()` | ner_extractor | Classify entities into predefined categories | entities: List[Entity] | List[ClassifiedEntity] | -| `RelationExtractor.find_relations()` | relation_extractor | Detect relationships between entities | text: str | List[Relation] | -| `RelationExtractor.classify_relations()` | relation_extractor | Classify relation types and directions | relations: List[Relation] | List[ClassifiedRelation] | -| `EventDetector.detect_events()` | event_detector | Identify events and their participants | text: str | List[Event] | -| `EventDetector.extract_temporal()` | event_detector | Extract temporal information for events | events: List[Event] | List[TemporalInfo] | -| `CorefResolver.resolve_references()` | coref_resolver | Resolve co-references and pronouns | text: str | List[Resolution] | -| `CorefResolver.link_entities()` | coref_resolver | Link entities across document sections | entities: List[Entity] | List[Link] | -| `TripleExtractor.extract_triples()` | triple_extractor | Extract RDF-style triples | text: str | List[Triple] | -| `TripleExtractor.validate_triples()` | triple_extractor | Validate triple structure and consistency | triples: List[Triple] | List[ValidatedTriple] | -| `LLMEnhancer.enhance_extraction()` | llm_enhancer | Use LLM for complex extraction tasks | text: str, task: str | EnhancedResults | -| `LLMEnhancer.detect_patterns()` | llm_enhancer | Identify complex patterns and relationships | text: str | List[Pattern] | -| `ExtractionValidator.validate_quality()` | extraction_validator | Assess extraction quality | results: ExtractionResults | QualityScore | -| `ExtractionValidator.filter_by_confidence()` | extraction_validator | Filter results by confidence score | results: List[Result], threshold: float | List[Result] | -| `ExtractionPipeline.run()` | semantic_extract | Execute complete extraction pipeline | text: str | ExtractionResults | - -#### 8. **Ontology Generation Functions** (`semantica.ontology`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `ClassInferrer.infer_classes()` | class_inferrer | Automatically discover entity classes | entities: List[Entity] | List[Class] | -| `ClassInferrer.build_hierarchy()` | class_inferrer | Build class inheritance hierarchy | classes: List[Class] | Hierarchy | -| `ClassInferrer.analyze_relationships()` | class_inferrer | Analyze class relationships and dependencies | classes: List[Class] | List[Relationship] | -| `PropertyGenerator.infer_properties()` | property_generator | Infer object and data properties | classes: List[Class] | List[Property] | -| `PropertyGenerator.detect_data_types()` | property_generator | Detect property data types and constraints | properties: List[Property] | List[DataType] | -| `PropertyGenerator.analyze_cardinality()` | property_generator | Analyze property cardinality | properties: List[Property] | List[Cardinality] | -| `OWLGenerator.generate_owl()` | owl_generator | Generate OWL ontology in RDF/XML format | ontology: Ontology | str | -| `OWLGenerator.serialize_rdf()` | owl_generator | Serialize to various RDF formats | ontology: Ontology, format: str | str | -| `BaseMapper.map_to_schema_org()` | base_mapper | Map entities to schema.org vocabulary | entities: List[Entity] | List[Mapping] | -| `BaseMapper.map_to_foaf()` | base_mapper | Map to FOAF ontology | entities: List[Entity] | List[Mapping] | -| `BaseMapper.map_to_dublin_core()` | base_mapper | Map to Dublin Core metadata standards | entities: List[Entity] | List[Mapping] | -| `VersionManager.create_version()` | version_manager | Create new ontology version | ontology: Ontology | Version | -| `VersionManager.track_changes()` | version_manager | Track changes between versions | old_version: Version, new_version: Version | List[Change] | -| `VersionManager.migrate_ontology()` | version_manager | Support ontology migration and updates | old_ontology: Ontology, new_schema: Schema | Ontology | -| `OntologyValidator.validate_schema()` | ontology_validator | Validate ontology schema consistency | ontology: Ontology | ValidationResult | -| `OntologyValidator.check_constraints()` | ontology_validator | Check ontology constraint violations | ontology: Ontology | List[Violation] | -| `DomainOntologies.get_finance_ontology()` | domain_ontologies | Get pre-built financial ontology | None | Ontology | -| `DomainOntologies.get_healthcare_ontology()` | domain_ontologies | Get pre-built healthcare ontology | None | Ontology | -| `OntologyManager.build_ontology()` | ontology | Build complete ontology from extracted data | data: ExtractedData | Ontology | -| `OntologyManager.export_ontology()` | ontology | Export ontology in various formats | ontology: Ontology, format: str | str | - -#### 9. **Knowledge Graph Functions** (`semantica.kg`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `GraphBuilder.create_node()` | graph_builder | Create knowledge graph node | id: str, type: str, properties: Dict | Node | -| `GraphBuilder.create_edge()` | graph_builder | Create relationship edge between nodes | from_node: str, to_node: str, relation: str | Edge | -| `GraphBuilder.build_subgraph()` | graph_builder | Build subgraph from specific entities | entities: List[Entity] | SubGraph | -| `GraphBuilder.merge_graphs()` | graph_builder | Merge multiple knowledge graphs | graphs: List[Graph] | Graph | -| `EntityResolver.resolve_identity()` | entity_resolver | Resolve entity identity across sources | entity1: Entity, entity2: Entity | Resolution | -| `EntityResolver.merge_entities()` | entity_resolver | Merge duplicate entities | entities: List[Entity] | MergedEntity | -| `EntityResolver.get_canonical()` | entity_resolver | Get canonical entity representation | entity: Entity | Entity | -| `Deduplicator.find_duplicates()` | deduplicator | Find duplicate entities | entities: List[Entity] | List[Duplicate] | -| `Deduplicator.merge_duplicates()` | deduplicator | Merge duplicate entities | duplicates: List[Duplicate] | List[MergedEntity] | -| `Deduplicator.validate_merge()` | deduplicator | Validate merge operation | merge: MergeOperation | ValidationResult | -| `SeedManager.load_seed_data()` | seed_manager | Load initial seed data | data_source: str | SeedData | -| `SeedManager.validate_seed_data()` | seed_manager | Validate seed data quality | seed_data: SeedData | ValidationResult | -| `SeedManager.update_seed_data()` | seed_manager | Update existing seed data | seed_data: SeedData | Status | -| `ProvenanceTracker.track_source()` | provenance_tracker | Track information source | information: Information | Provenance | -| `ProvenanceTracker.get_provenance()` | provenance_tracker | Retrieve provenance information | info_id: str | Provenance | -| `ProvenanceTracker.calculate_confidence()` | provenance_tracker | Calculate confidence scores | provenance: Provenance | float | -| `ConflictDetector.detect_conflicts()` | conflict_detector | Detect conflicts between sources | sources: List[Source] | List[Conflict] | -| `ConflictDetector.classify_severity()` | conflict_detector | Classify conflict severity | conflict: Conflict | Severity | -| `ConflictDetector.create_resolution_workflow()` | conflict_detector | Create resolution workflow | conflicts: List[Conflict] | Workflow | -| `GraphValidator.validate_consistency()` | graph_validator | Validate graph consistency | graph: Graph | ValidationResult | -| `GraphValidator.check_schema_compliance()` | graph_validator | Check schema compliance | graph: Graph, schema: Schema | ComplianceResult | -| `GraphValidator.calculate_quality_metrics()` | graph_validator | Calculate quality metrics | graph: Graph | QualityMetrics | -| `GraphAnalyzer.calculate_centrality()` | graph_analyzer | Calculate node centrality | graph: Graph | CentralityScores | -| `GraphAnalyzer.detect_communities()` | graph_analyzer | Detect community structures | graph: Graph | List[Community] | -| `GraphAnalyzer.analyze_connectivity()` | graph_analyzer | Analyze graph connectivity | graph: Graph | ConnectivityMetrics | -| `KnowledgeGraphManager.build_graph()` | kg | Build complete knowledge graph | data: ProcessedData | KnowledgeGraph | -| `KnowledgeGraphManager.export_graph()` | kg | Export graph in various formats | graph: Graph, format: str | str | -| `KnowledgeGraphManager.visualize_graph()` | kg | Generate graph visualizations | graph: Graph | Visualization | - -#### 10. **Vector Store Functions** (`semantica.vector_store`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `PineconeAdapter.connect()` | pinecone_adapter | Connect to Pinecone service | api_key: str | Connection | -| `PineconeAdapter.create_index()` | pinecone_adapter | Create new vector index | name: str, dimension: int | Index | -| `PineconeAdapter.upsert_vectors()` | pinecone_adapter | Insert or update vectors | vectors: List[Vector], metadata: Dict | Status | -| `PineconeAdapter.query_vectors()` | pinecone_adapter | Query similar vectors | query_vector: Vector, top_k: int | List[Result] | -| `FAISSAdapter.create_index()` | faiss_adapter | Create FAISS index | index_type: str, dimension: int | Index | -| `FAISSAdapter.add_vectors()` | faiss_adapter | Add vectors to index | vectors: List[Vector] | Status | -| `FAISSAdapter.search_similar()` | faiss_adapter | Search for similar vectors | query_vector: Vector, k: int | List[Result] | -| `FAISSAdapter.save_index()` | faiss_adapter | Save index to disk | file_path: str | Status | -| `MilvusAdapter.create_collection()` | milvus_adapter | Create Milvus collection | name: str, schema: Schema | Collection | -| `MilvusAdapter.insert_vectors()` | milvus_adapter | Insert vectors into collection | vectors: List[Vector] | Status | -| `MilvusAdapter.search_vectors()` | milvus_adapter | Search vectors in collection | query_vector: Vector, top_k: int | List[Result] | -| `WeaviateAdapter.create_schema()` | weaviate_adapter | Create Weaviate schema | schema: Schema | Status | -| `WeaviateAdapter.add_objects()` | weaviate_adapter | Add objects to Weaviate | objects: List[Object] | Status | -| `WeaviateAdapter.graphql_query()` | weaviate_adapter | Execute GraphQL queries | query: str | QueryResult | -| `QdrantAdapter.create_collection()` | qdrant_adapter | Create Qdrant collection | name: str, config: Dict | Collection | -| `QdrantAdapter.upsert_points()` | qdrant_adapter | Insert or update points | points: List[Point] | Status | -| `QdrantAdapter.search_points()` | qdrant_adapter | Search points with filters | query: Vector, filters: Dict | List[Result] | -| `NamespaceManager.create_namespace()` | namespace_manager | Create isolated namespace | name: str | Namespace | -| `NamespaceManager.set_access_control()` | namespace_manager | Set namespace permissions | namespace: str, permissions: Dict | Status | -| `NamespaceManager.list_namespaces()` | namespace_manager | List available namespaces | None | List[Namespace] | -| `MetadataStore.index_metadata()` | metadata_store | Index metadata for search | metadata: Dict | Status | -| `MetadataStore.filter_by_metadata()` | metadata_store | Filter results by metadata | filters: Dict | List[Result] | -| `MetadataStore.search_metadata()` | metadata_store | Search metadata content | query: str | List[Result] | -| `HybridSearch.combine_results()` | hybrid_search | Combine vector and metadata results | vector_results: List, metadata_results: List | List[Result] | -| `HybridSearch.rank_results()` | hybrid_search | Rank results using multiple criteria | results: List[Result] | List[RankedResult] | -| `HybridSearch.fuse_results()` | hybrid_search | Fuse results from different sources | results: List[List[Result]] | List[FusedResult] | -| `IndexOptimizer.optimize_index()` | index_optimizer | Optimize index performance | index: Index | OptimizedIndex | -| `IndexOptimizer.rebuild_index()` | index_optimizer | Rebuild index for better performance | index: Index | Index | -| `IndexOptimizer.get_performance_metrics()` | index_optimizer | Get index performance metrics | index: Index | Metrics | -| `VectorStoreManager.get_store_info()` | vector_store | Get store information | None | StoreInfo | -| `VectorStoreManager.backup_store()` | vector_store | Create store backup | backup_path: str | Status | -| `VectorStoreManager.restore_store()` | vector_store | Restore from backup | backup_path: str | Status | - -#### 11. **Triple Store Functions** (`semantica.triple_store`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `BlazegraphAdapter.connect()` | blazegraph_adapter | Connect to Blazegraph instance | endpoint: str | Connection | -| `BlazegraphAdapter.execute_sparql()` | blazegraph_adapter | Execute SPARQL queries | query: str | QueryResult | -| `BlazegraphAdapter.bulk_load()` | blazegraph_adapter | Load triples in bulk | triples: List[Triple] | Status | -| `JenaAdapter.create_model()` | jena_adapter | Create and manage RDF models | None | Model | -| `JenaAdapter.add_triples()` | jena_adapter | Add triples to model | model: Model, triples: List[Triple] | Status | -| `JenaAdapter.run_inference()` | jena_adapter | Execute inference rules | model: Model | InferredModel | -| `RDF4JAdapter.create_repository()` | rdf4j_adapter | Create and configure repositories | config: Dict | Repository | -| `RDF4JAdapter.begin_transaction()` | rdf4j_adapter | Start transaction for batch operations | None | Transaction | -| `GraphDBAdapter.enable_reasoning()` | graphdb_adapter | Enable reasoning capabilities | config: Dict | Status | -| `GraphDBAdapter.visualize_graph()` | graphdb_adapter | Generate graph visualizations | query: str | Visualization | -| `VirtuosoAdapter.connect_cluster()` | virtuoso_adapter | Connect to Virtuoso cluster | cluster_config: Dict | Connection | -| `VirtuosoAdapter.optimize_queries()` | virtuoso_adapter | Optimize query performance | queries: List[str] | OptimizedQueries | -| `TripleManager.add_triple()` | triple_manager | Add single triple to store | triple: Triple | Status | -| `TripleManager.add_triples()` | triple_manager | Add multiple triples | triples: List[Triple] | Status | -| `TripleManager.delete_triple()` | triple_manager | Delete specific triple | triple: Triple | Status | -| `TripleManager.update_triple()` | triple_manager | Update existing triple | old_triple: Triple, new_triple: Triple | Status | -| `QueryEngine.execute_sparql()` | query_engine | Execute SPARQL queries | query: str | QueryResult | -| `QueryEngine.optimize_query()` | query_engine | Optimize query for performance | query: str | OptimizedQuery | -| `QueryEngine.format_results()` | query_engine | Format query results | results: QueryResult, format: str | FormattedResults | -| `BulkLoader.load_file()` | bulk_loader | Load triples from file | file_path: str | Status | -| `BulkLoader.create_indexes()` | bulk_loader | Create database indexes | None | Status | -| `BulkLoader.monitor_progress()` | bulk_loader | Monitor loading progress | None | Progress | -| `TripleStoreManager.get_store_info()` | triple_store | Get store statistics and status | None | StoreInfo | -| `TripleStoreManager.backup_store()` | triple_store | Create backup of store | backup_path: str | Status | -| `TripleStoreManager.restore_store()` | triple_store | Restore from backup | backup_path: str | Status | - -#### 12. **Embeddings Functions** (`semantica.embeddings`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `TextEmbedder.embed_text()` | text_embedder | Generate text embeddings | text: str | Vector | -| `TextEmbedder.embed_sentence()` | text_embedder | Generate sentence-level embeddings | sentence: str | Vector | -| `TextEmbedder.embed_document()` | text_embedder | Generate document-level embeddings | document: str | Vector | -| `ImageEmbedder.embed_image()` | image_embedder | Generate image embeddings | image_path: str | Vector | -| `ImageEmbedder.extract_features()` | image_embedder | Extract visual features | image_path: str | Features | -| `ImageEmbedder.embed_batch()` | image_embedder | Process multiple images | image_paths: List[str] | List[Vector] | -| `AudioEmbedder.embed_audio()` | audio_embedder | Generate audio embeddings | audio_path: str | Vector | -| `AudioEmbedder.extract_audio_features()` | audio_embedder | Extract audio features | audio_path: str | Features | -| `MultimodalEmbedder.fuse_embeddings()` | multimodal_embedder | Fuse multiple modality embeddings | embeddings: List[Vector] | FusedVector | -| `MultimodalEmbedder.align_modalities()` | multimodal_embedder | Align different modality representations | modalities: List[Vector] | AlignedVectors | -| `ContextManager.set_window_size()` | context_manager | Set context window size | size: int | None | -| `ContextManager.apply_sliding_window()` | context_manager | Apply sliding window approach | text: str | List[Window] | -| `ContextManager.manage_attention()` | context_manager | Manage attention mechanisms | config: Dict | AttentionWeights | -| `PoolingStrategies.mean_pooling()` | pooling_strategies | Apply mean pooling strategy | vectors: List[Vector] | Vector | -| `PoolingStrategies.max_pooling()` | pooling_strategies | Apply max pooling strategy | vectors: List[Vector] | Vector | -| `PoolingStrategies.attention_pooling()` | pooling_strategies | Apply attention-based pooling | vectors: List[Vector], weights: List[float] | Vector | -| `ProviderAdapter.connect_openai()` | provider_adapter | Connect to OpenAI embedding API | api_key: str | Connection | -| `ProviderAdapter.connect_bge()` | provider_adapter | Connect to BGE embedding service | endpoint: str | Connection | -| `ProviderAdapter.connect_llama()` | provider_adapter | Connect to Llama embedding model | model_path: str | Connection | -| `ProviderAdapter.load_custom_model()` | provider_adapter | Load custom embedding model | model_config: Dict | Model | -| `EmbeddingOptimizer.optimize_dimensions()` | embedding_optimizer | Optimize embedding dimensions | vectors: List[Vector], target_dim: int | OptimizedVectors | -| `EmbeddingOptimizer.apply_clustering()` | embedding_optimizer | Apply clustering to embeddings | vectors: List[Vector] | ClusterResults | -| `EmbeddingOptimizer.calculate_similarity()` | embedding_optimizer | Calculate embedding similarities | vector1: Vector, vector2: Vector | float | -| `SemanticEmbedder.generate_embeddings()` | embeddings | Generate embeddings for input | input_data: Any | List[Vector] | -| `SemanticEmbedder.batch_process()` | embeddings | Process multiple inputs in batch | inputs: List[Any] | List[Vector] | -| `SemanticEmbedder.get_embedding_stats()` | embeddings | Get embedding statistics | None | Stats | - -#### 13. **RAG System Functions** (`semantica.qa_rag`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `RAGManager.process_question()` | qa_rag | Process user question | question: str | Answer | -| `RAGManager.get_answer()` | qa_rag | Get RAG-generated answer | question: str | Answer | -| `RAGManager.evaluate_performance()` | qa_rag | Evaluate RAG performance | test_data: List[Question] | PerformanceMetrics | -| `SemanticChunker.chunk_text()` | semantic_chunker | Create semantic chunks with context | text: str | List[Chunk] | -| `SemanticChunker.optimize_chunks()` | semantic_chunker | Optimize chunk size and overlap | chunks: List[Chunk] | List[OptimizedChunk] | -| `SemanticChunker.merge_chunks()` | semantic_chunker | Merge related chunks when needed | chunks: List[Chunk] | List[MergedChunk] | -| `PromptTemplates.get_template()` | prompt_templates | Get RAG prompt template | template_name: str | Template | -| `PromptTemplates.format_question()` | prompt_templates | Format question for retrieval | question: str | FormattedQuestion | -| `PromptTemplates.inject_context()` | prompt_templates | Inject retrieved context into prompt | question: str, context: str | Prompt | -| `RetrievalPolicies.set_strategy()` | retrieval_policies | Set retrieval strategy | strategy: str | None | -| `RetrievalPolicies.rank_results()` | retrieval_policies | Rank retrieval results | results: List[Result] | List[RankedResult] | -| `RetrievalPolicies.filter_results()` | retrieval_policies | Filter results by criteria | results: List[Result], criteria: Dict | List[FilteredResult] | -| `AnswerBuilder.construct_answer()` | answer_builder | Construct answer from retrieved context | context: List[Chunk], question: str | Answer | -| `AnswerBuilder.integrate_context()` | answer_builder | Integrate multiple context sources | contexts: List[Context] | IntegratedContext | -| `AnswerBuilder.attribute_sources()` | answer_builder | Attribute answer to source documents | answer: Answer | AttributedAnswer | -| `ProvenanceTracker.track_sources()` | provenance_tracker | Track information sources | answer: Answer | List[Source] | -| `ProvenanceTracker.calculate_confidence()` | provenance_tracker | Calculate answer confidence | answer: Answer | float | -| `ProvenanceTracker.link_evidence()` | provenance_tracker | Link answer to supporting evidence | answer: Answer | List[Evidence] | -| `AnswerValidator.validate_answer()` | answer_validator | Validate answer accuracy | answer: Answer | ValidationResult | -| `AnswerValidator.fact_check()` | answer_validator | Perform fact checking | answer: Answer | FactCheckResult | -| `AnswerValidator.verify_consistency()` | answer_validator | Verify answer consistency | answer: Answer | ConsistencyResult | -| `RAGOptimizer.optimize_retrieval()` | rag_optimizer | Optimize retrieval performance | config: Dict | OptimizedConfig | -| `RAGOptimizer.enhance_queries()` | rag_optimizer | Enhance user queries | query: str | EnhancedQuery | -| `RAGOptimizer.improve_ranking()` | rag_optimizer | Improve result ranking | results: List[Result] | List[ImprovedResult] | -| `ConversationManager.start_conversation()` | conversation_manager | Start new conversation | None | Conversation | -| `ConversationManager.add_context()` | conversation_manager | Add context to conversation | conversation: Conversation, context: str | None | -| `ConversationManager.get_history()` | conversation_manager | Get conversation history | conversation: Conversation | List[Message] | - -#### 14. **Reasoning Engine Functions** (`semantica.reasoning`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `InferenceEngine.add_rule()` | inference_engine | Add inference rule to engine | rule: Rule | Status | -| `InferenceEngine.execute_rules()` | inference_engine | Execute inference rules | None | List[Inference] | -| `InferenceEngine.forward_chain()` | inference_engine | Perform forward chaining | None | List[Inference] | -| `InferenceEngine.backward_chain()` | inference_engine | Perform backward chaining | goal: Goal | List[Inference] | -| `InferenceEngine.resolve_conflicts()` | inference_engine | Resolve rule conflicts | conflicts: List[Conflict] | Resolution | -| `SPARQLReasoner.expand_query()` | sparql_reasoner | Expand SPARQL query with reasoning | query: str | ExpandedQuery | -| `SPARQLReasoner.infer_results()` | sparql_reasoner | Infer additional results | query_results: QueryResult | InferredResults | -| `SPARQLReasoner.apply_reasoning()` | sparql_reasoner | Apply reasoning to query results | query: str, results: QueryResult | ReasonedResults | -| `ReteEngine.compile_rules()` | rete_engine | Compile rules into Rete network | rules: List[Rule] | ReteNetwork | -| `ReteEngine.match_patterns()` | rete_engine | Match patterns using Rete algorithm | facts: List[Fact] | List[Match] | -| `ReteEngine.execute_matches()` | rete_engine | Execute matched rules | matches: List[Match] | List[Inference] | -| `AbductiveReasoner.generate_hypotheses()` | abductive_reasoner | Generate explanatory hypotheses | observations: List[Observation] | List[Hypothesis] | -| `AbductiveReasoner.find_explanations()` | abductive_reasoner | Find explanations for observations | observations: List[Observation] | List[Explanation] | -| `AbductiveReasoner.rank_hypotheses()` | abductive_reasoner | Rank hypotheses by plausibility | hypotheses: List[Hypothesis] | List[RankedHypothesis] | -| `DeductiveReasoner.apply_logic()` | deductive_reasoner | Apply logical inference rules | premises: List[Premise] | List[Conclusion] | -| `DeductiveReasoner.prove_theorem()` | deductive_reasoner | Prove logical theorems | theorem: Theorem | Proof | -| `DeductiveReasoner.validate_argument()` | deductive_reasoner | Validate logical arguments | argument: Argument | ValidationResult | -| `RuleManager.define_rule()` | rule_manager | Define new inference rule | rule_definition: str | Rule | -| `RuleManager.validate_rule()` | rule_manager | Validate rule syntax and logic | rule: Rule | ValidationResult | -| `RuleManager.track_execution()` | rule_manager | Track rule execution history | rule: Rule | ExecutionHistory | -| `ReasoningValidator.validate_reasoning()` | reasoning_validator | Validate reasoning process | reasoning: Reasoning | ValidationResult | -| `ReasoningValidator.check_consistency()` | reasoning_validator | Check reasoning consistency | reasoning: Reasoning | ConsistencyResult | -| `ReasoningValidator.detect_errors()` | reasoning_validator | Detect reasoning errors | reasoning: Reasoning | List[Error] | -| `ExplanationGenerator.generate_explanation()` | explanation_generator | Generate reasoning explanation | reasoning: Reasoning | Explanation | -| `ExplanationGenerator.show_reasoning_path()` | explanation_generator | Show reasoning path | reasoning: Reasoning | ReasoningPath | -| `ExplanationGenerator.justify_conclusion()` | explanation_generator | Justify reasoning conclusion | conclusion: Conclusion | Justification | -| `ReasoningManager.run_reasoning()` | reasoning | Run complete reasoning process | input_data: Any | ReasoningResult | -| `ReasoningManager.get_reasoning_results()` | reasoning | Get reasoning results | reasoning_id: str | ReasoningResult | -| `ReasoningManager.export_reasoning()` | reasoning | Export reasoning process | reasoning: Reasoning, format: str | str | - -#### 15. **Multi-Agent System Functions** (`semantica.agents`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `AgentManager.register_agent()` | agent_manager | Register new agent | agent_config: Dict | Agent | -| `AgentManager.start_agent()` | agent_manager | Start agent execution | agent: Agent | Status | -| `AgentManager.stop_agent()` | agent_manager | Stop agent execution | agent_id: str | Status | -| `AgentManager.monitor_agent()` | agent_manager | Monitor agent status | agent_id: str | AgentStatus | -| `OrchestrationEngine.coordinate_agents()` | orchestration_engine | Coordinate multiple agents | agents: List[Agent] | Coordination | -| `OrchestrationEngine.distribute_tasks()` | orchestration_engine | Distribute tasks among agents | tasks: List[Task], agents: List[Agent] | TaskDistribution | -| `OrchestrationEngine.manage_workflows()` | orchestration_engine | Manage agent workflows | workflow: Workflow | WorkflowStatus | -| `ToolRegistry.register_tool()` | tool_registry | Register tool for agent use | tool: Tool | Status | -| `ToolRegistry.discover_tools()` | tool_registry | Discover available tools | None | List[Tool] | -| `ToolRegistry.get_tool()` | tool_registry | Get specific tool | tool_name: str | Tool | -| `CostTracker.monitor_costs()` | cost_tracker | Monitor agent execution costs | agent_id: str | CostMetrics | -| `CostTracker.set_budget()` | cost_tracker | Set cost budget limits | budget: float | Status | -| `CostTracker.optimize_resources()` | cost_tracker | Optimize resource usage | usage_data: Dict | OptimizationPlan | -| `SandboxManager.create_sandbox()` | sandbox_manager | Create agent sandbox | config: Dict | Sandbox | -| `SandboxManager.isolate_agent()` | sandbox_manager | Isolate agent execution | agent: Agent | IsolationStatus | -| `SandboxManager.set_resource_limits()` | sandbox_manager | Set resource limits | limits: Dict | Status | -| `WorkflowEngine.define_workflow()` | workflow_engine | Define agent workflow | workflow_definition: Dict | Workflow | -| `WorkflowEngine.execute_workflow()` | workflow_engine | Execute defined workflow | workflow: Workflow | WorkflowResult | -| `WorkflowEngine.monitor_progress()` | workflow_engine | Monitor workflow progress | workflow_id: str | Progress | -| `AgentCommunication.send_message()` | agent_communication | Send message between agents | from_agent: str, to_agent: str, message: Message | Status | -| `AgentCommunication.route_message()` | agent_communication | Route message to appropriate agent | message: Message | RoutingResult | -| `AgentCommunication.manage_protocols()` | agent_communication | Manage communication protocols | protocols: List[Protocol] | Status | -| `PolicyEnforcer.enforce_policy()` | policy_enforcer | Enforce access policies | agent: Agent, resource: Resource | EnforcementResult | -| `PolicyEnforcer.check_compliance()` | policy_enforcer | Check policy compliance | agent: Agent | ComplianceResult | -| `PolicyEnforcer.set_permissions()` | policy_enforcer | Set agent permissions | agent: Agent, permissions: List[Permission] | Status | -| `AgentAnalytics.analyze_performance()` | agent_analytics | Analyze agent performance | agent_id: str | PerformanceMetrics | -| `AgentAnalytics.analyze_behavior()` | agent_analytics | Analyze agent behavior patterns | agent_id: str | BehaviorAnalysis | -| `AgentAnalytics.optimize_agents()` | agent_analytics | Optimize agent performance | agents: List[Agent] | OptimizationPlan | -| `MultiAgentManager.create_team()` | multi_agent_manager | Create agent team | team_config: Dict | Team | -| `MultiAgentManager.orchestrate_workflow()` | multi_agent_manager | Orchestrate team workflow | team: Team, workflow: Workflow | WorkflowResult | -| `MultiAgentManager.get_team_status()` | multi_agent_manager | Get team execution status | team_id: str | TeamStatus | - -#### 16. **Domain Specialization Functions** (`semantica.domains`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `FinanceSpecialist.analyze_financial_data()` | finance | Analyze financial documents and data | data: FinancialData | Analysis | -| `FinanceSpecialist.extract_financial_entities()` | finance | Extract financial entities and metrics | text: str | List[FinancialEntity] | -| `FinanceSpecialist.calculate_ratios()` | finance | Calculate financial ratios | data: FinancialData | List[Ratio] | -| `HealthcareSpecialist.process_medical_records()` | healthcare | Process medical records and documents | records: MedicalRecords | ProcessedRecords | -| `HealthcareSpecialist.extract_medical_entities()` | healthcare | Extract medical entities and concepts | text: str | List[MedicalEntity] | -| `HealthcareSpecialist.analyze_drug_interactions()` | healthcare | Analyze drug interaction patterns | drugs: List[Drug] | List[Interaction] | -| `LegalSpecialist.analyze_legal_documents()` | legal | Analyze legal documents and contracts | documents: LegalDocuments | Analysis | -| `LegalSpecialist.extract_legal_entities()` | legal | Extract legal entities and clauses | text: str | List[LegalEntity] | -| `LegalSpecialist.identify_risks()` | legal | Identify legal risks and compliance issues | document: LegalDocument | List[Risk] | -| `ScientificSpecialist.process_research_papers()` | scientific | Process scientific research papers | papers: ResearchPapers | ProcessedPapers | -| `ScientificSpecialist.extract_scientific_entities()` | scientific | Extract scientific entities and concepts | text: str | List[ScientificEntity] | -| `ScientificSpecialist.analyze_citations()` | scientific | Analyze citation networks and patterns | papers: List[Paper] | CitationAnalysis | -| `DomainManager.register_domain()` | domain_manager | Register new domain specialization | domain_config: Dict | Domain | -| `DomainManager.get_domain_processor()` | domain_manager | Get domain-specific processor | domain: str | Processor | -| `DomainManager.list_domains()` | domain_manager | List available domains | None | List[Domain] | -| `DomainValidator.validate_domain_data()` | domain_validator | Validate domain-specific data | data: Any, domain: str | ValidationResult | -| `DomainValidator.check_compliance()` | domain_validator | Check domain compliance | data: Any, domain: str | ComplianceResult | -| `DomainOptimizer.optimize_for_domain()` | domain_optimizer | Optimize processing for specific domain | config: Dict, domain: str | OptimizedConfig | -| `DomainOptimizer.adapt_models()` | domain_optimizer | Adapt models for domain requirements | models: List[Model], domain: str | AdaptedModels | - -#### 17. **User Interface Functions** (`semantica.ui`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `WebInterface.start_server()` | web_interface | Start web interface server | config: Dict | Server | -| `WebInterface.create_dashboard()` | web_interface | Create interactive dashboard | dashboard_config: Dict | Dashboard | -| `WebInterface.add_widget()` | web_interface | Add widget to dashboard | widget: Widget | Status | -| `CLIInterface.create_command()` | cli_interface | Create CLI command | command_config: Dict | Command | -| `CLIInterface.add_subcommand()` | cli_interface | Add subcommand to CLI | subcommand: SubCommand | Status | -| `CLIInterface.setup_help()` | cli_interface | Setup command help and documentation | command: Command | Status | -| `APIInterface.create_endpoint()` | api_interface | Create REST API endpoint | endpoint_config: Dict | Endpoint | -| `APIInterface.add_middleware()` | api_interface | Add middleware to API | middleware: Middleware | Status | -| `APIInterface.generate_docs()` | api_interface | Generate API documentation | None | Documentation | -| `VisualizationEngine.create_chart()` | visualization_engine | Create data visualization chart | chart_config: Dict | Chart | -| `VisualizationEngine.create_graph()` | visualization_engine | Create knowledge graph visualization | graph: Graph | GraphViz | -| `VisualizationEngine.export_visualization()` | visualization_engine | Export visualization to file | visualization: Visualization, format: str | Status | -| `UIThemeManager.set_theme()` | ui_theme_manager | Set UI theme and styling | theme: Theme | Status | -| `UIThemeManager.customize_colors()` | ui_theme_manager | Customize color scheme | colors: ColorScheme | Status | -| `UIThemeManager.apply_responsive_design()` | ui_theme_manager | Apply responsive design | breakpoints: List[Breakpoint] | Status | -| `UserManager.create_user()` | user_manager | Create new user account | user_data: Dict | User | -| `UserManager.authenticate_user()` | user_manager | Authenticate user login | credentials: Credentials | AuthResult | -| `UserManager.set_permissions()` | user_manager | Set user permissions | user: User, permissions: List[Permission] | Status | -| `SessionManager.create_session()` | session_manager | Create user session | user: User | Session | -| `SessionManager.validate_session()` | session_manager | Validate session token | token: str | ValidationResult | -| `SessionManager.refresh_session()` | session_manager | Refresh session token | session: Session | NewSession | -| `UIComponentManager.register_component()` | ui_component_manager | Register UI component | component: Component | Status | -| `UIComponentManager.get_component()` | ui_component_manager | Get component by name | name: str | Component | -| `UIComponentManager.render_component()` | ui_component_manager | Render component with data | component: Component, data: Any | RenderedComponent | - -#### 18. **Operations Functions** (`semantica.ops`) - -| Function | Module | Description | Parameters | Returns | -|----------|--------|-------------|------------|---------| -| `DeploymentManager.deploy_service()` | deployment_manager | Deploy service to production | service_config: Dict | Deployment | -| `DeploymentManager.rollback_deployment()` | deployment_manager | Rollback to previous version | deployment_id: str | Status | -| `DeploymentManager.scale_service()` | deployment_manager | Scale service instances | service: Service, instances: int | Status | -| `MonitoringManager.setup_monitoring()` | monitoring_manager | Setup system monitoring | config: Dict | Monitoring | -| `MonitoringManager.create_alert()` | monitoring_manager | Create monitoring alert | alert_config: Dict | Alert | -| `MonitoringManager.get_metrics()` | monitoring_manager | Get system metrics | time_range: TimeRange | Metrics | -| `LoggingManager.configure_logging()` | logging_manager | Configure logging system | config: Dict | Status | -| `LoggingManager.create_log_handler()` | logging_manager | Create custom log handler | handler_config: Dict | LogHandler | -| `LoggingManager.analyze_logs()` | logging_manager | Analyze log patterns | logs: List[Log] | LogAnalysis | -| `BackupManager.create_backup()` | backup_manager | Create system backup | backup_config: Dict | Backup | -| `BackupManager.restore_backup()` | backup_manager | Restore from backup | backup_id: str | Status | -| `BackupManager.schedule_backup()` | backup_manager | Schedule automatic backups | schedule: Schedule | Status | -| `SecurityManager.audit_security()` | security_manager | Perform security audit | None | AuditResult | -| `SecurityManager.scan_vulnerabilities()` | security_manager | Scan for security vulnerabilities | None | VulnerabilityReport | -| `SecurityManager.update_policies()` | security_manager | Update security policies | policies: List[Policy] | Status | -| `PerformanceManager.optimize_performance()` | performance_manager | Optimize system performance | config: Dict | OptimizationResult | -| `PerformanceManager.benchmark_system()` | performance_manager | Benchmark system performance | None | BenchmarkResult | -| `PerformanceManager.profile_application()` | performance_manager | Profile application performance | app: Application | ProfileResult | -| `ResourceManager.allocate_resources()` | resource_manager | Allocate system resources | resource_config: Dict | ResourceAllocation | -| `ResourceManager.monitor_usage()` | resource_manager | Monitor resource usage | None | UsageMetrics | -| `ResourceManager.optimize_allocation()` | resource_manager | Optimize resource allocation | usage_data: UsageData | OptimizationPlan | -| `OpsManager.deploy_infrastructure()` | ops_manager | Deploy infrastructure components | infra_config: Dict | Infrastructure | -| `OpsManager.manage_services()` | ops_manager | Manage service lifecycle | services: List[Service] | ServiceStatus | -| `OpsManager.get_operational_status()` | ops_manager | Get operational status | None | OperationalStatus | - -| `CLIManager.handle_errors()` | cli_manager | Handle CLI errors | error: Exception | ErrorResponse | - ---- - -## 📊 Function Statistics - -| Module | Total Functions | Core Functions | Utility Functions | Management Functions | -|--------|----------------|----------------|-------------------|---------------------| -| **Core Engine** | 13 | 6 | 4 | 3 | -| **Pipeline Builder** | 13 | 7 | 3 | 3 | -| **Data Ingestion** | 12 | 8 | 2 | 2 | -| **Document Parsing** | 16 | 12 | 2 | 2 | -| **Text Normalization** | 14 | 10 | 2 | 2 | -| **Text Chunking** | 14 | 8 | 4 | 2 | -| **Semantic Extraction** | 15 | 10 | 3 | 2 | -| **Ontology Generation** | 20 | 12 | 4 | 4 | -| **Knowledge Graph** | 25 | 15 | 6 | 4 | -| **Vector Store** | 25 | 15 | 6 | 4 | -| **Triple Store** | 25 | 15 | 6 | 4 | -| **Embeddings** | 20 | 12 | 4 | 4 | -| **RAG System** | 20 | 12 | 4 | 4 | -| **Reasoning Engine** | 25 | 15 | 6 | 4 | -| **Multi-Agent System** | 25 | 15 | 6 | 4 | - -**Total: 20 Modules, 400+ Functions** - ---- - -## 📥 Import Reference - -### Complete Import Guide - -```python -# ============================================================================= -# CORE MODULES -# ============================================================================= - -# Main Semantica class -from semantica import Semantica -from semantica.core import Config, PluginManager, Orchestrator, LifecycleManager - -# Pipeline management -from semantica.pipeline import ( - PipelineBuilder, ExecutionEngine, FailureHandler, - ParallelismManager, ResourceScheduler, PipelineValidator, - MonitoringHooks, PipelineTemplates, PipelineManager -) - -# ============================================================================= -# DATA PROCESSING MODULES -# ============================================================================= - -# Data ingestion -from semantica.ingest import ( - FileIngestor, WebIngestor, FeedIngestor, StreamIngestor, - RepoIngestor, EmailIngestor, DBIngestor, IngestManager, - ConnectorRegistry -) - -# Document parsing -from semantica.parse import ( - PDFParser, DOCXParser, PPTXParser, ExcelParser, HTMLParser, - JSONLParser, CSVParser, LaTeXParser, ImageParser, TableParser, - ParserRegistry -) - -# Text normalization -from semantica.normalize import ( - TextCleaner, LanguageDetector, EncodingHandler, EntityNormalizer, - DateNormalizer, NumberNormalizer, NormalizationPipeline -) - -# Text chunking -from semantica.split import ( - SlidingWindowChunker, SemanticChunker, StructuralChunker, - TableChunker, ProvenanceTracker, ChunkValidator, SplitManager -) - -# ============================================================================= -# SEMANTIC INTELLIGENCE MODULES -# ============================================================================= - -# Semantic extraction -from semantica.semantic_extract import ( - NERExtractor, RelationExtractor, EventDetector, CorefResolver, - TripleExtractor, LLMEnhancer, ExtractionValidator, ExtractionPipeline -) - -# Ontology generation -from semantica.ontology import ( - OntologyGenerator, ClassInferrer, PropertyGenerator, OWLGenerator, - BaseMapper, VersionManager, OntologyValidator, DomainOntologies, - OntologyManager -) - -# Knowledge graph -from semantica.kg import ( - GraphBuilder, EntityResolver, Deduplicator, SeedManager, - ProvenanceTracker, ConflictDetector, GraphValidator, GraphAnalyzer, - KnowledgeGraphManager -) - -# ============================================================================= -# STORAGE & RETRIEVAL MODULES -# ============================================================================= - -# Vector stores -from semantica.vector_store import ( - PineconeAdapter, FAISSAdapter, MilvusAdapter, WeaviateAdapter, - QdrantAdapter, NamespaceManager, MetadataStore, HybridSearch, - IndexOptimizer, VectorStoreManager -) - -# Triple stores -from semantica.triple_store import ( - BlazegraphAdapter, JenaAdapter, RDF4JAdapter, GraphDBAdapter, - VirtuosoAdapter, TripleManager, QueryEngine, BulkLoader, - TripleStoreManager -) - -# Embeddings -from semantica.embeddings import ( - SemanticEmbedder, TextEmbedder, ImageEmbedder, AudioEmbedder, - MultimodalEmbedder, ContextManager, PoolingStrategies, - ProviderAdapter, EmbeddingOptimizer -) - -# ============================================================================= -# AI & REASONING MODULES -# ============================================================================= - -# RAG system -from semantica.qa_rag import ( - RAGManager, SemanticChunker, PromptTemplates, RetrievalPolicies, - AnswerBuilder, ProvenanceTracker, AnswerValidator, RAGOptimizer, - ConversationManager -) - -# Reasoning engine -from semantica.reasoning import ( - InferenceEngine, SPARQLReasoner, ReteEngine, AbductiveReasoner, - DeductiveReasoner, RuleManager, ReasoningValidator, ExplanationGenerator, - ReasoningManager -) - -# Multi-agent system -from semantica.agents import ( - AgentManager, OrchestrationEngine, ToolRegistry, CostTracker, - SandboxManager, WorkflowEngine, AgentCommunication, PolicyEnforcer, - AgentAnalytics, MultiAgentManager -) - - -# ============================================================================= -# UTILITY MODULES -# ============================================================================= - -# Additional utilities -from semantica.utils import ( - DataValidator, SchemaManager, TemplateManager, SeedManager, - SemanticDeduplicator, ConflictDetector, MultiProviderConfig, - AnalyticsDashboard, BusinessIntelligenceDashboard, - HealthcareProcessor, EnterpriseDeployment -) - -# ============================================================================= -# QUICK IMPORTS FOR COMMON USE CASES -# ============================================================================= - -# Basic usage -from semantica import Semantica -from semantica.processors import DocumentProcessor, WebProcessor, FeedProcessor -from semantica.context import ContextEngineer -from semantica.embeddings import SemanticEmbedder -from semantica.graph import KnowledgeGraphBuilder -from semantica.query import SPARQLQueryGenerator -from semantica.pipelines import ResearchPipeline, BusinessIntelligenceDashboard -from semantica.healthcare import HealthcareProcessor -from semantica.deployment import EnterpriseDeployment -from semantica.analytics import AnalyticsDashboard - -# Advanced usage -from semantica.config import MultiProviderConfig -``` - ---- - -## 🔧 Module Dependencies - -### Core Dependencies -```python -# Required for all modules -semantica[core] >= 1.0.0 - -# Optional dependencies by module -semantica[pdf] # PDF parsing -semantica[web] # Web scraping -semantica[feeds] # RSS/Atom feeds -semantica[office] # Office documents -semantica[scientific] # Scientific formats -semantica[all] # All dependencies -``` - -### External Dependencies -```python -# Vector stores -pinecone-client >= 2.0.0 -faiss-cpu >= 1.7.0 -weaviate-client >= 3.0.0 - -# Triple stores -rdflib >= 6.0.0 -sparqlwrapper >= 2.0.0 - -# ML/AI -openai >= 1.0.0 -transformers >= 4.20.0 -torch >= 1.12.0 - -# Data processing -pandas >= 1.5.0 -numpy >= 1.21.0 -spacy >= 3.4.0 -``` - ---- - -## 📊 Module Statistics - -- **Total Main Modules**: 20 -- **Total Submodules**: 120+ -- **Total Classes**: 200+ -- **Total Functions**: 1000+ -- **Supported Formats**: 50+ -- **Supported Languages**: 100+ -- **Integration Points**: 30+ - ---- - -## 🎯 Solving Real-World Knowledge Graph Challenges - -> **Complete solution for the fundamental problems in building production-ready Knowledge Graphs** - -### **Problem 1: Stick to a Fixed Template** -```python -from semantica.templates import SchemaTemplate, SchemaEnforcer - -# Define your business-specific schema -business_schema = SchemaTemplate( - name="company_knowledge_graph", - entities=["Company", "Person", "Product", "Department", "Project", "Quarterly_Report"], - relationships=["founded_by", "works_for", "manages", "belongs_to", "reports_to", "produces"], - constraints={ - "Company": {"required_props": ["name", "industry", "founded_year", "headquarters"]}, - "Person": {"required_props": ["name", "title", "department", "employee_id"]}, - "Quarterly_Report": {"required_props": ["quarter", "year", "revenue", "company"]}, - "founded_by": {"domain": "Company", "range": "Person"}, - "produces": {"domain": "Company", "range": "Product"} - } -) - -# Enforce consistent structure -schema_enforcer = SchemaEnforcer() -enforced_entities = schema_enforcer.enforce_entity_schema(extracted_entities, business_schema) -enforced_relationships = schema_enforcer.enforce_relationship_schema(extracted_relations, business_schema) -``` - -### **Problem 2: Start with What We Already Know** -```python -from semantica.seed import SeedDataManager, KnowledgeSeeder - -# Load your existing verified data -seed_manager = SeedDataManager() -seed_manager.load_products("verified_products.csv") -seed_manager.load_departments("org_chart.json") -seed_manager.load_employees("hr_database") - -# Seed the knowledge graph with verified foundation -knowledge_seeder = KnowledgeSeeder() -seeded_graph = knowledge_seeder.seed_entities(seed_data, verification_rules=True) -seeded_graph = knowledge_seeder.seed_relationships(org_chart, hierarchy_rules=True) - -# Build on this foundation -integrator = DataIntegrator() -final_graph = integrator.integrate_seed_with_extracted(seeded_graph, extracted_data) -``` - -### **Problem 3: Clean Up and Merge Duplicates** -```python -from semantica.deduplication import DuplicateDetector, EntityMerger, SimilarityEngine - -# Detect semantic duplicates -duplicate_detector = DuplicateDetector() -duplicates = duplicate_detector.find_semantic_duplicates(entities) -# This will find "First Quarter Sales" and "Q1 Sales Report" as duplicates - -# Advanced similarity detection -similarity_engine = SimilarityEngine() -similarity = similarity_engine.calculate_semantic_similarity("First Quarter Sales", "Q1 Sales Report") -# Returns high similarity score (e.g., 0.92) - -# Merge duplicates intelligently -entity_merger = EntityMerger() -merged = entity_merger.merge_duplicates(duplicates, strategy="highest_confidence") -merged = entity_merger.merge_with_validation(duplicates, validation_rules) -``` - -### **Problem 4: Flag When Sources Disagree** -```python -from semantica.conflicts import ConflictDetector, SourceTracker, DisagreementResolver - -# Detect conflicting information -conflict_detector = ConflictDetector() -conflicts = conflict_detector.detect_value_conflicts(entities, "sales_figure") -# Finds $10M vs $12M sales figures - -# Track exact sources -source_tracker = SourceTracker() -sources = source_tracker.track_property_sources(property, "sales_figure", "$10M") -# Returns: [{"document": "Q1_Report.pdf", "page": 5, "section": "Financial Summary"}] - -# Flag for investigation -disagreement_resolver = DisagreementResolver() -flagged = disagreement_resolver.flag_for_investigation(conflicts) -# Generates investigation report with exact document locations -``` - -### **Complete Production-Ready Solution** -```python -from semantica import Semantica -from semantica.templates import SchemaTemplate -from semantica.seed import SeedDataManager -from semantica.deduplication import DuplicateDetector, EntityMerger -from semantica.conflicts import ConflictDetector, SourceTracker -from semantica.kg_qa import KGQualityAssessor - -# Initialize Semantica with quality assurance -core = Semantica( - llm_provider="openai", - embedding_model="text-embedding-3-large", - vector_store="pinecone", - graph_db="neo4j", - quality_assurance=True, # Enable all QA features - conflict_detection=True, # Enable conflict detection - deduplication=True # Enable advanced deduplication -) - -# 1. Define your business schema -business_schema = SchemaTemplate.load("business_schema.yaml") - -# 2. Seed with verified data -seed_manager = SeedDataManager() -seed_manager.load_verified_data("verified_entities.json") -seeded_graph = seed_manager.create_foundation_graph(business_schema) - -# 3. Process documents with quality controls -knowledge_base = core.build_knowledge_base( - sources=["documents/"], - schema_template=business_schema, - seed_data=seeded_graph, - enable_deduplication=True, - enable_conflict_detection=True, - enable_quality_assurance=True -) - -# 4. Get quality report -kg_qa = KGQualityAssessor() -quality_report = kg_qa.generate_quality_report(knowledge_base) -print(f"Knowledge Graph Quality Score: {quality_report.overall_score}") -print(f"Duplicates Found: {quality_report.duplicates_count}") -print(f"Conflicts Detected: {quality_report.conflicts_count}") -print(f"Source Disagreements: {quality_report.disagreements_count}") - -# 5. Get investigation guide for conflicts -if quality_report.conflicts_count > 0: - investigation_guide = quality_report.get_investigation_guide() - print("Conflicts requiring investigation:") - for conflict in investigation_guide.conflicts: - print(f"- {conflict.property}: {conflict.values}") - print(f" Sources: {conflict.source_documents}") -``` - ---- - -*This comprehensive module reference covers all major components of the Semantica toolkit. Each module is designed to be modular, extensible, and production-ready for enterprise use cases. The toolkit specifically addresses the fundamental challenges in building Knowledge Graphs that are consistent, reliable, and production-ready.*