ORIGYN API

High-Accuracy AI Content Detection Backend API
Powered by an ensemble of 23 independent detection engines — neural classifiers, statistical tests, and linguistic forensics. Run locally or deploy anywhere via Docker.

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Features

• 23 Independent Detection Engines: Neural classifiers (DeBERTa, RoBERTa, E5 LoRA), statistical distributions (GLTR, Perplexity, Binoculars), and structural heuristics.
• Pure REST & Streaming API: FastAPI-powered with sub-second quick-scoring and real-time Server-Sent Events (SSE) streaming.
• Zero Third-Party Data Sharing: Runs fully containerized on your infrastructure.
• Hardware Adaptive: Auto-detects available CPU/RAM/GPU and selects optimal profiles (lite, standard, performance).
• Production Ready: Pre-configured with Docker, health checks, configurable CORS, and asynchronous queue management.

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Quick Start

1. Local Development (Python 3.10+)

# Create and activate virtual environment
python -m venv venv

# Windows:
.\venv\Scripts\Activate.ps1
# Linux/macOS:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Start API server
python -m uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

Interactive API documentation will be available at: http://localhost:8000/docs

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2. Docker & Docker Compose

# Build and run with persistent storage for models & database
docker compose up -d

Check server health:

curl http://localhost:8000/health

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Hardware Profiles

Configure the resource footprint via the ORIGYN_PROFILE environment variable in your .env or container settings:

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API Endpoints

All endpoints accept JSON payloads. Text inputs must be at least 50 characters.

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Integration Examples

Fast Scan (POST /api/quick-score)

JavaScript / TypeScript

const res = await fetch("http://localhost:8000/api/quick-score", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    text: "Artificial intelligence has rapidly evolved over recent years, transforming numerous industries and reshaping everyday technology."
    // Or URL: url: "https://example.com/article"
  })
});

const data = await res.json();
console.log(`Score: ${data.score}/100 | Verdict: ${data.verdict}`);

Python

import requests

res = requests.post(
    "http://localhost:8000/api/quick-score",
    json={"text": "Artificial intelligence has rapidly evolved over recent years..."}
)
print(res.json())

Response:

{
  "score": 88.5,
  "verdict": "Likely AI-generated",
  "confidence": 0.94,
  "raw": {
    "classifier_desklib": 0.91,
    "classifier_e5": 0.89,
    "classifier_superannotate": 0.87,
    "classifier_remodetect": 0.88
  }
}

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Real-Time Streaming Scan (SSE)

For a live user experience where engines report results as they finish:

1. Start analysis:

   curl -X POST "http://localhost:8000/api/analyze/start" \
     -H "Content-Type: application/json" \
     -d '{"text": "Sample text exceeding 50 characters..."}'
   # Returns: {"status": "started", "report_id": "ab12cd34"}
   

2. Connect to SSE stream:

   const eventSource = new EventSource("http://localhost:8000/api/stream/ab12cd34");
   
   eventSource.onmessage = (event) => {
     const data = JSON.parse(event.data);
     if (data.done) {
       eventSource.close();
       console.log("Analysis complete!");
     } else {
       console.log(`Engine ${data.engine_name}: ${data.score} (${data.verdict})`);
       console.log(`Running overall score: ${data.overall_score}`);
     }
   };
   

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Deploy to Microsoft Azure

Azure Container Apps (Recommended)

# 1. Login to Azure
az login

# 2. Create resource group & Azure Container Registry (ACR)
az group create --name origyn-rg --location eastus
az acr create --resource-group origyn-rg --name origynacr$RANDOM --sku Basic --admin-enabled true

# 3. Build container directly in Azure cloud (no local Docker required)
az acr build --registry <YOUR_ACR_NAME> --image origyn-api:latest .

# 4. Create Container App environment
az containerapp env create \
  --name origyn-env \
  --resource-group origyn-rg \
  --location eastus

# 5. Deploy the API
az containerapp create \
  --name origyn-api \
  --resource-group origyn-rg \
  --environment origyn-env \
  --image <YOUR_ACR_NAME>.azurecr.io/origyn-api:latest \
  --target-port 8000 \
  --ingress external \
  --cpu 2.0 \
  --memory 4.0Gi \
  --env-vars ORIGYN_PROFILE=lite ORIGYN_CORS_ORIGINS="*"

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Configuration Reference

Set these in your .env or container environment variables:

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License

MIT License.