Guide

Requirements

Package	Required	Purpose
oridecon	Yes	Core framework
oridecon-contracts	Yes	Protocol definitions
oridecon-ai-llm	Yes	LLM client integration
oridecon-ai-rag	Yes	RAG pipeline integration
oridecon-ai-feedback	Yes	Feedback collection
oridecon-ai-observability	Yes	Observability
oridecon-ai-agents	Optional	Agent system (runtime discovery)
oridecon-ai-skills	Optional	Skills system (runtime discovery)
oridecon-ai-memory	Optional	Memory system (runtime discovery)
oridecon-ai-session	Optional	Session management (runtime discovery)
oridecon-ai-mcp	Optional	MCP protocol (runtime discovery)
oridecon-ai-workers	Optional	Background workers (runtime discovery)

What problem does it solve?

oridecon-ai is the orchestration layer for the AI subsystem. Instead of wiring LLM clients, vector stores, RAG pipelines, and observability providers individually, you configure a single AIConfig and AIModule wires everything through the DI container.

It discovers sub-packages via Python entry points (oridecon.ai.subsystems), so installing a supported extension (e.g., oridecon-ai-llm) is enough — no manual registration.

Mental model

AIConfig ──► AIModule.configure()
                  │
           AIProvider (register)
           ├── LLMProvider        (oridecon-ai-llm)
           ├── VectorProvider     (oridecon-vector, optional)
           ├── RAGProvider        (oridecon-ai-rag, optional)
           ├── Observability      (always on: metrics, tracing, health)
           ├── Governance         (optional)
           └── Entry-point discovery (any subsystem with ep group)

AIModule is the public entrypoint. AIProvider is the internal orchestrator that delegates to sub-providers.

Core concepts

AIConfig

The single config object that drives the entire AI subsystem. It nests sub-configs for each subsystem:

from oridecon.ai.config import AIConfig
from oridecon.ai.llm import ClientConfig

config = AIConfig(
    enabled=True,
    llm=ClientConfig(provider="openai", model="gpt-4o"),
    # vector=VectorConfig(...),
    # rag=RAGConfig(...),
)

Config is read from ORI_AI__* environment variables or the ai: section of application.yaml.

AIProvider

The provider class that orchestrates sub-providers. In register() it:

1. Registers monitoring singletons (AIHealthMonitor, AIMetrics, AITracer, CallbackManagerProtocol)
2. Delegates LLM, Vector, and RAG to their respective sub-providers
3. Discovers additional subsystems via oridecon.ai.subsystems entry points
4. Wires governance and RAG cache when the relevant config is present

from oridecon.ai.di.provider import AIProvider

provider = AIProvider(config=AIConfig(llm=ClientConfig(provider="openai")))
# Used internally by AIModule — you normally don't instantiate it directly

Entry-point discovery

Any installed package declaring the oridecon.ai.subsystems entry-point group is automatically discovered:

# pyproject.toml of a subsystem package
[project.entry-points."oridecon.ai.subsystems"]
llm = "oridecon.ai.llm.di.provider:LLMProvider"

The AIProvider.register() method loads each entry point and calls sub_provider.register(container).

Typical usage

1. Basic LLM access

from oridecon import Application
from oridecon.ai.module import AIModule
from oridecon.ai.llm import ClientConfig
from oridecon.contracts.ai import LLMClientProtocol


async def main() -> None:
    config = AIConfig(llm=ClientConfig(provider="openai", model="gpt-4o"))
    async with Application.boot(
        name="ai-demo",
        modules=[AIModule.configure(config)],
    ) as app:
        llm = await app.container.resolve(LLMClientProtocol)
        result = await llm.complete([{"role": "user", "content": "Hello!"}])
        if result.is_ok():
            print(result.unwrap().content)

2. Health check

from oridecon.contracts.core import HealthStatus

health = await app.health_check()
print(health.status)  # HealthStatus.HEALTHY or .DEGRADED

3. Manual provider wiring (no module)

from oridecon import Application
from oridecon.ai.di.provider import AIProvider
from oridecon.ai.config import AIConfig

app = Application(name="ai-demo")
app.add_provider(AIProvider(config=AIConfig(llm=ClientConfig(provider="openai"))))
await app.start()

Best practices

• Start with AIModule.configure() — it’s the intended public API. Only drop to AIProvider directly when you need fine-grained control over sub-provider configuration.
• One AIConfig per app — the AI subsystem is designed as a singleton. Creating multiple instances will register duplicate bindings.
• Use env vars for secrets — set ORI_AI_LLM__API_KEY instead of hardcoding API keys. AIConfig reads environment variables automatically.
• Enable governance in production — AIConfig(governance=GovernanceConfig(enabled=True)) provides an audit trail for AI operations.
• Don’t import sub-packages directly — resolve LLMClientProtocol through the container instead of importing LLMProvider or individual clients.

Next steps

• Architecture — provider lifecycle and entry-point discovery
• Configuration — every config key
• How-tos — common recipes
• LLM Package — the LLM client layer in depth