API Reference Overview

This section provides a detailed, parameter-level reference for all public APIs of the Ona Intelligence Layer. It is designed for Developers who are building applications on top of our platform.

SDK (Recommended)

The fastest way to use these APIs is through the Ona SDK, which handles authentication, retries, cursor pagination, and streaming for you.

Install:

# Python
git clone https://github.com/AsobaCloud/sdk.git && cd sdk/python && pip3 install -e .

# JavaScript
git clone https://github.com/AsobaCloud/sdk.git && cd sdk/javascript && npm install

Live APIs available now:

API	SDK client	Endpoint
Inverter Telemetry	`client.inverter_telemetry` / `sdk.inverterTelemetry`	`https://af5jy5ob3e.execute-api.af-south-1.amazonaws.com/prod`
OODA Terminal Alerts	`client.ooda_terminal` / `sdk.oodaTerminal`	`https://3lpq00xevg.execute-api.af-south-1.amazonaws.com/prod`

See the SDK repository for full examples.

What You Can Find Here

Authentication & Setup

Authentication: Learn how to authenticate your requests to the API.

Forecasting APIs

Forecasting API: Generate energy production forecasts
Freemium Forecast: Generate a 24-hour forecast from a CSV file (free tier)

Terminal API (OODA Workflow)

The Terminal API provides endpoints for the complete OODA (Observe-Orient-Decide-Act) workflow:

Terminal API Overview: Complete reference for OODA workflow APIs
Asset Management: Create, list, and retrieve solar assets
Fault Detection: Run fault detection on assets (Observe)
AI Diagnostics: Execute AI diagnostics on faults (Orient)
Maintenance Scheduling: Create maintenance schedules (Decide)
Bill of Materials: Generate BOMs for maintenance
Work Orders: Create and manage work orders (Act)
Job Tracking: Subscribe to job status updates

ML Integration APIs

Forecast Results: Retrieve stored ML forecast results
Interpolation Results: Retrieve gap-filling interpolation results
ML Model Registry: Access catalog of available ML models
OODA Summaries: Retrieve ML-enhanced OODA summaries with severity and energy-at-risk

Data Ingestion APIs

Data Ingestion API Overview: Upload historical and real-time data
Upload Training Data: Upload historical data for model training
Upload Nowcast Data: Upload real-time data for forecasting

API Design Principles

Our APIs are designed to be predictable and intuitive. We follow standard RESTful conventions and use standard HTTP response codes to indicate API errors.

Request Format

All POST requests should be sent with a Content-Type of application/json or multipart/form-data, depending on the endpoint.

Response Format

All API responses, including errors, are returned in JSON format. A successful response will have a status of "success". An error response will have a status of "error" and include a descriptive error message.

Example Success Response:

{
  "status": "success",
  "data": { ... }
}

Example Error Response:

{
  "status": "error",
  "error": "A description of what went wrong."
}