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:

• Semantica orchestrator coordinates the full KG construction pipeline from a YAML config
• ConfigManager loads YAML config with deep-merge, validation, and environment variable overrides
• PluginRegistry enables dynamic component registration and loading at runtime
• LifecycleManager manages startup/shutdown with health monitoring and lifecycle hooks

Use individual modules directly for the vast majority of use cases. Reach for `semantica.core` only when you need application-level lifecycle management, centralized config, or a plugin system.

What You Get

• Semantica — High-level orchestrator: coordinates the full KG construction pipeline from a single config.yaml. Entry point for application-level deployments.
• ConfigManager — YAML config with deep-merge, SEMANTICA_ env var overrides, and dot-notation nested key access. Keeps secrets out of source files.
• LifecycleManager — Ordered startup/shutdown hooks, health monitoring, and a 6-state machine. Essential for long-running services like FastAPI apps.
• PluginRegistry — Register custom ingestors, parsers, exporters, or any component. Load them by name at runtime: no imports required.

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

Semantica (Orchestration)

Semantica is the 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
  # Do not put API keys in YAML: use environment variables instead.
  # ConfigManager loads YAML with yaml.safe_load(), which does not
  # interpolate ${...} expressions. Set secrets via env vars (see below).

processing:
  batch_size: 32
  max_workers: 4

quality:
  min_confidence: 0.7

logging:
  level: INFO

Environment variable overrides (prefix SEMANTICA_):

Use double underscores (__) to produce a dot separator for nested key access. The implementation strips the SEMANTICA_ prefix, lowercases the result, and replaces __ with . before calling set_nested_value() on the config dict.

# Double underscores map to nested keys:
export SEMANTICA_LLM_PROVIDER__MODEL=gpt-4o
export SEMANTICA_LLM_PROVIDER__NAME=openai
export SEMANTICA_PROCESSING__BATCH_SIZE=64
export SEMANTICA_QUALITY__MIN_CONFIDENCE=0.8
export SEMANTICA_LOGGING__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()
# → {
#     "state": "ready",
#     "is_healthy": True,
#     "total_components": 1,
#     "healthy_components": 1,
#     "unhealthy_components": 0,
#     "last_check": 1234567890.0,
#     "components": {
#         "database": {"healthy": True, "message": "Connected", "timestamp": ...}
#     }
#   }

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 — Pipeline execution and step orchestration.
• Utils — Shared utilities used by Core internally.
• Getting Started — Learn the basics before using Core.
• LLMs — Configure LLM providers via ConfigManager.