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* docs: replace Exported Classes import blocks with summary tables across all 25 modules * docs: add method/parameter tables to parse, ingest, ontology, normalize, triplet_store, change_management, conflicts, export, graph_store, provenance, and semantic_extract modules
211 lines
6.3 KiB
Markdown
211 lines
6.3 KiB
Markdown
---
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title: "Core Module"
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description: "Framework orchestration, lifecycle management, configuration, and plugin system."
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icon: "gear"
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---
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`semantica.core` is the coordination layer for the framework. For most tasks you should use individual modules directly (`semantica.ingest`, `semantica.kg`, etc.). Reach for Core when you need application-level lifecycle management, centralized configuration, or a plugin registry.
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## Exported Classes
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| Class | Role |
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| --- | --- |
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| `Semantica` | Orchestration entry point — coordinates the full KG construction pipeline |
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| `ConfigManager` | YAML config loading, deep-merge, validation, and env var overrides |
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| `LifecycleManager` | Startup/shutdown state machine with health monitoring and lifecycle hooks |
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| `PluginRegistry` | Dynamic plugin discovery, registration, and loading |
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| `method_registry` | Global `MethodRegistry` instance — register and dispatch custom orchestration methods |
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<Tip>
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**Use individual modules directly** for the vast majority of use cases. Use the `Semantica` orchestration class only when you need application-level lifecycle management or a plugin system.
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</Tip>
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## Semantica (Orchestration)
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High-level entry point that coordinates the full KG construction pipeline:
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```python
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from semantica.core import Semantica, ConfigManager
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config_manager = ConfigManager()
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config = config_manager.load_from_file("config.yaml")
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framework = Semantica(config=config)
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framework.initialize()
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try:
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result = framework.build_knowledge_base(
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sources=["doc1.pdf", "doc2.docx"],
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embeddings=True,
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graph=True,
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)
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status = framework.get_status()
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print(f"State: {status['state']}")
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finally:
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framework.shutdown(graceful=True)
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```
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### Core Methods
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| Method | Description |
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| ------ | ----------- |
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| `initialize()` | Initialize all framework components |
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| `build_knowledge_base(sources, **kwargs)` | Orchestrate full KG construction pipeline |
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| `run_pipeline(pipeline, data)` | Execute an existing `Pipeline` instance |
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| `get_status()` | Return system health and current state |
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| `shutdown(graceful=True)` | Graceful shutdown — waits for in-flight operations |
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## ConfigManager
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Centralized config loading with deep-merge and environment variable overrides:
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```python
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from semantica.core import ConfigManager
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manager = ConfigManager()
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config = manager.load_from_file("config.yaml")
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# Merge base config with environment-specific overrides
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merged = manager.merge_configs(
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manager.load_from_file("base.yaml"),
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manager.load_from_file("prod.yaml"),
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)
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# Nested key access with dot notation
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batch_size = config.get("processing.batch_size", default=16)
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config.set("processing.batch_size", 64)
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config.validate()
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```
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### YAML Configuration
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```yaml
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llm_provider:
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name: openai
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model: gpt-4o
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api_key: ${OPENAI_API_KEY}
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processing:
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batch_size: 32
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max_workers: 4
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quality:
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min_confidence: 0.7
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logging:
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level: INFO
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```
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Environment variable overrides (prefix `SEMANTICA_`):
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```bash
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export SEMANTICA_PROCESSING_BATCH_SIZE=64
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export SEMANTICA_LOG_LEVEL=DEBUG
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```
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## LifecycleManager
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Manages framework state with a defined state machine and ordered startup/shutdown hooks:
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**State machine:** `UNINITIALIZED` → `INITIALIZING` → `READY` → `RUNNING` → `STOPPING` → `STOPPED`
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```python
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from semantica.core import LifecycleManager
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manager = LifecycleManager()
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def init_db():
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print("Initializing database...")
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def cleanup_db():
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print("Closing database connections...")
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# Lower priority values run first during startup
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# Higher priority values run first during shutdown
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manager.register_startup_hook(init_db, priority=10)
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manager.register_shutdown_hook(cleanup_db, priority=10)
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manager.startup()
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# Component health monitoring
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class DatabaseComponent:
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def health_check(self):
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return {"healthy": True, "message": "Connected"}
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manager.register_component("database", DatabaseComponent())
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summary = manager.get_health_summary()
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# → {"database": {"healthy": True, "message": "Connected"}, ...}
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manager.shutdown(graceful=True)
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```
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## PluginRegistry
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Register custom components that participate in the full pipeline — provenance tracking, retry policies, and parallel execution included:
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```python
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from semantica.core import PluginRegistry
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class MyPlugin:
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def initialize(self):
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print("Plugin initialized")
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def execute(self, data):
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return {"processed": True}
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registry = PluginRegistry(plugin_paths=["./plugins"])
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registry.register_plugin("my_plugin", MyPlugin, version="1.0.0")
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plugin = registry.load_plugin("my_plugin", api_key="xxx")
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result = plugin.execute("sample data")
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for info in registry.list_plugins():
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print(f"{info['name']}: {info['version']}")
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```
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## MethodRegistry
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Register custom orchestration methods and dispatch them by name:
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```python
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from semantica.core import method_registry
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from semantica.core.methods import build_knowledge_base
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def fast_kb_builder(sources, **kwargs):
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# Custom logic — skip embeddings for speed
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...
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method_registry.register("knowledge_base", "fast", fast_kb_builder)
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result = build_knowledge_base(sources=["doc.pdf"], method="fast")
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```
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## When to Use Core vs. Individual Modules
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| Scenario | Recommended Approach |
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| -------- | -------------------- |
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| Single extraction task | `from semantica.semantic_extract import NERExtractor` |
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| Build a knowledge graph | `from semantica.kg import GraphBuilder` |
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| Multi-step pipeline | `from semantica.pipeline import Pipeline` |
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| App-level lifecycle + config | `from semantica.core import Semantica, ConfigManager` |
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| Custom dispatch / plugins | `from semantica.core import method_registry, PluginRegistry` |
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<Tip>
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Use `Semantica` and `LifecycleManager` only when building a long-running application (e.g. a FastAPI service) that needs ordered startup, health checks, and graceful shutdown. For scripts and notebooks, use individual modules directly.
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</Tip>
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<CardGroup cols={2}>
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<Card title="Pipeline" icon="arrows-turn-to-dots" href="pipeline">
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Pipeline execution and step orchestration.
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</Card>
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<Card title="Utils" icon="wrench" href="utils">
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Shared utilities used by Core internally.
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</Card>
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<Card title="Getting Started" icon="play" href="../getting-started">
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Learn the basics before using Core.
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</Card>
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<Card title="LLMs" icon="microchip" href="llms">
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Configure LLM providers via ConfigManager.
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</Card>
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</CardGroup>
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