Configuration

The config section key is ai (configures AIConfig). Environment prefix: ORI_AI__.

AIConfig

Key	Type	Default	Env Var	Description
name	str	"ai"	ORI_AI__NAME	Configuration name
enabled	bool	True	ORI_AI__ENABLED	Enable AI features
llm	ClientConfig | None	None	ORI_AI_LLM__*	LLM client configuration
vector	VectorConfig | None	None	ORI_VECTOR__*	Vector store configuration
rag	RAGConfig | None	None	ORI_AI_RAG__*	RAG pipeline configuration
governance	GovernanceConfig	default-factory (disabled when governance package absent)	ORI_AI_GOVERNANCE__*	AI governance configuration
observability	ObservabilityConfig	default-factory (disabled if oridecon-ai-observability not installed)	ORI_AI_OBSERVABILITY__*	AI observability configuration
subsystems	dict[str, dict[str, Any]]	{}	ORI_AI__SUBSYSTEMS__*	Dynamic config for third-party subsystems

Example YAML

application.yaml

ai:
  enabled: true
  llm:
    provider: openai
    model: gpt-4o
    api_key: ${ORI_AI_LLM__API_KEY}
    temperature: 0.7
    timeout: 60
  governance:
    enabled: true
  subsystems:
    custom_subsystem:
      option_a: value

Env-var overrides

The nested delimiter is __ (double underscore):

export ORI_AI__ENABLED=true
export ORI_AI_LLM__PROVIDER=anthropic
export ORI_AI_LLM__MODEL=claude-sonnet-4-20250514
export ORI_AI_LLM__API_KEY=sk-ant-...
export ORI_AI_LLM__TEMPERATURE=0.3

Subsystem configuration

Use get_subsystem_config() to retrieve config for a dynamically-discovered subsystem:

from oridecon.ai.config import AIConfig, get_subsystem_config

config = AIConfig()
sub_cfg = get_subsystem_config(config, "llm")
# Returns config.llm if set, otherwise config.subsystems["llm"]

Production security

AIConfig includes a model_validator that blocks insecure API keys (sk-..., sk-test, change-me) when ORI_ENV=production.