semantica/docs/reference/core.md

title, description, icon

title	description	icon
Core Module	Framework orchestration, lifecycle management, configuration, and plugin system.	gear

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.

Exported Classes

Class	Role
Semantica	Orchestration entry point — coordinates the full KG construction pipeline
ConfigManager	YAML config loading, deep-merge, validation, and env var overrides
LifecycleManager	Startup/shutdown state machine with health monitoring and lifecycle hooks
PluginRegistry	Dynamic plugin discovery, registration, and loading
method_registry	Global MethodRegistry instance — register and dispatch custom orchestration methods

**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.

Semantica (Orchestration)

High-level entry point that coordinates the full KG construction pipeline:

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)

Core Methods

Method	Description
initialize()	Initialize all framework components
build_knowledge_base(sources, **kwargs)	Orchestrate full KG construction pipeline
run_pipeline(pipeline, data)	Execute an existing Pipeline instance
get_status()	Return system health and current state
shutdown(graceful=True)	Graceful shutdown — waits for in-flight operations

ConfigManager

Centralized config loading with deep-merge and environment variable overrides:

from semantica.core import ConfigManager

manager = ConfigManager()
config = manager.load_from_file("config.yaml")

# Merge base config with environment-specific overrides
merged = manager.merge_configs(
    manager.load_from_file("base.yaml"),
    manager.load_from_file("prod.yaml"),
)

# Nested key access with dot notation
batch_size = config.get("processing.batch_size", default=16)
config.set("processing.batch_size", 64)
config.validate()

YAML Configuration

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

Environment variable overrides (prefix SEMANTICA_):

export SEMANTICA_PROCESSING_BATCH_SIZE=64
export SEMANTICA_LOG_LEVEL=DEBUG

LifecycleManager

Manages framework state with a defined state machine and ordered startup/shutdown hooks:

State machine: UNINITIALIZED → INITIALIZING → READY → RUNNING → STOPPING → STOPPED

from semantica.core import LifecycleManager

manager = LifecycleManager()

def init_db():
    print("Initializing database...")

def cleanup_db():
    print("Closing database connections...")

# Lower priority values run first during startup
# Higher priority values run first during shutdown
manager.register_startup_hook(init_db,     priority=10)
manager.register_shutdown_hook(cleanup_db, priority=10)

manager.startup()

# Component health monitoring
class DatabaseComponent:
    def health_check(self):
        return {"healthy": True, "message": "Connected"}

manager.register_component("database", DatabaseComponent())
summary = manager.get_health_summary()
# → {"database": {"healthy": True, "message": "Connected"}, ...}

manager.shutdown(graceful=True)

PluginRegistry

Register custom components that participate in the full pipeline — provenance tracking, retry policies, and parallel execution included:

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 and dispatch them by name:

from semantica.core import method_registry
from semantica.core.methods import build_knowledge_base

def fast_kb_builder(sources, **kwargs):
    # Custom logic — skip embeddings for speed
    ...

method_registry.register("knowledge_base", "fast", fast_kb_builder)

result = build_knowledge_base(sources=["doc.pdf"], method="fast")

When to Use Core vs. Individual Modules

Scenario	Recommended Approach
Single extraction task	from semantica.semantic_extract import NERExtractor
Build a knowledge graph	from semantica.kg import GraphBuilder
Multi-step pipeline	from semantica.pipeline import Pipeline
App-level lifecycle + config	from semantica.core import Semantica, ConfigManager
Custom dispatch / plugins	from semantica.core import method_registry, PluginRegistry

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. Pipeline execution and step orchestration. Shared utilities used by Core internally. Learn the basics before using Core. Configure LLM providers via ConfigManager.