--- title: "Core Module" description: "Framework orchestration, lifecycle management, configuration, and plugin system." icon: "gear" --- > Framework infrastructure for complex workflows — lifecycle hooks, centralized config, and a plugin registry. --- ## Overview The **Core Module** is the coordination layer for Semantica. For most tasks you should use individual modules directly; reach for Core when you need lifecycle management, centralized configuration, or multi-step orchestration. **Use individual modules directly** (`semantica.ingest`, `semantica.kg`, etc.) for the vast majority of use cases. Use the `Semantica` orchestration class only when you need application-level lifecycle management or a plugin system. Orchestration class for coordinating complex multi-module workflows. Unified configuration loading, validation, and merging. Startup/shutdown hooks and system health monitoring. Dynamic plugin discovery, registration, and loading. --- ## Semantica (Orchestration) ```python from semantica.core import Semantica, ConfigManager config_manager = ConfigManager() config = config_manager.load_from_file("config.yaml") framework = Semantica(config=config) framework.initialize() try: result = framework.build_knowledge_base( sources=["doc1.pdf", "doc2.docx"], embeddings=True, graph=True, ) status = framework.get_status() print(f"State: {status['state']}") finally: framework.shutdown(graceful=True) ``` | Method | Description | |--------|-------------| | `initialize()` | Initialize all framework components | | `build_knowledge_base(sources, **kwargs)` | Orchestrate KG construction | | `run_pipeline(pipeline, data)` | Execute a processing pipeline | | `get_status()` | System health and state | | `shutdown(graceful=True)` | Graceful shutdown | --- ## ConfigManager ```python from semantica.core import ConfigManager manager = ConfigManager() config = manager.load_from_file("config.yaml") # Merge base + override configs merged = manager.merge_configs( manager.load_from_file("base.yaml"), manager.load_from_file("prod.yaml"), ) # Nested access batch_size = config.get("processing.batch_size", default=16) config.set("processing.batch_size", 64) config.validate() ``` ### YAML Configuration ```yaml llm_provider: name: openai model: gpt-4o api_key: ${OPENAI_API_KEY} processing: batch_size: 32 max_workers: 4 quality: min_confidence: 0.7 logging: level: INFO ``` ```bash # Environment variable overrides (SEMANTICA_ prefix) export SEMANTICA_PROCESSING_BATCH_SIZE=64 export SEMANTICA_LOG_LEVEL=DEBUG ``` --- ## LifecycleManager State machine: `UNINITIALIZED` → `INITIALIZING` → `READY` → `RUNNING` → `STOPPING` → `STOPPED` ```python from semantica.core import LifecycleManager manager = LifecycleManager() def init_db(): print("Initializing database...") def cleanup_db(): print("Closing database connections...") manager.register_startup_hook(init_db, priority=10) manager.register_shutdown_hook(cleanup_db, priority=10) manager.startup() # Health monitoring class DatabaseComponent: def health_check(self): return {"healthy": True, "message": "Connected"} manager.register_component("database", DatabaseComponent()) summary = manager.get_health_summary() manager.shutdown(graceful=True) ``` Lower `priority` values execute first during startup; higher values execute first during shutdown. --- ## PluginRegistry ```python from semantica.core import PluginRegistry class MyPlugin: def initialize(self): print("Plugin initialized") def execute(self, data): return {"processed": True} registry = PluginRegistry(plugin_paths=["./plugins"]) registry.register_plugin("my_plugin", MyPlugin, version="1.0.0") plugin = registry.load_plugin("my_plugin", api_key="xxx") result = plugin.execute("sample data") for info in registry.list_plugins(): print(f"{info['name']}: {info['version']}") ``` --- ## MethodRegistry Register custom orchestration methods for extensibility. ```python from semantica.core import method_registry def fast_kb_builder(sources, **kwargs): # custom logic — skip embeddings for speed ... method_registry.register("knowledge_base", "fast", fast_kb_builder) from semantica.core.methods import build_knowledge_base result = build_knowledge_base(sources=["doc.pdf"], method="fast") ``` --- ## See Also Pipeline execution and orchestration. Shared utilities used by Core internally. Learn the basics before using Core. Configure LLM providers via ConfigManager.