praisonai-svc 1.2.0

A unified framework that turns any PraisonAI Python package into a web service on Azure

PraisonAI Service Framework

A unified framework that turns any PraisonAI Python package into a web service on Azure using just one file per package.

Features

✅ One-file service creation - Only an app.py file needed
✅ Azure-native - Uses Container Apps, Blob Storage, Queue, Table Storage
✅ Cost-predictable - Scale-to-zero, hard-capped replicas (£15-25/month)
✅ Production-ready - Retry logic, idempotency, monitoring
✅ Secure - API keys, rate limiting, CORS
✅ Fast - Built with FastAPI and async/await

Quick Start

Installation

# Using uv (recommended)
uv pip install praisonai-svc

# Using pip
pip install praisonai-svc

Create a New Service

praisonai-svc new my-service
cd my-service

Implement Your Application

Edit app.py - customize the job processing logic:

from dotenv import load_dotenv
from praisonai_svc import ServiceApp

# Load environment variables from .env file
load_dotenv()

app = ServiceApp("My Service")

@app.job
def process_job(payload: dict) -> tuple[bytes, str, str]:
    """Process job and return file data."""
    # Your processing logic here
    title = payload.get('title', 'Untitled')
    content = f"Processed: {title}\n\nFull payload:\n{payload}"
    
    return (
        content.encode(),  # File content as bytes
        "text/plain",      # Content type
        "result.txt"       # Filename
    )

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8080)

Configure Environment

Create .env:

PRAISONAI_AZURE_STORAGE_CONNECTION_STRING=your_connection_string
PRAISONAI_API_KEY=your_secret_key

Run the Service

python app.py

Test the API

# Create a job
curl -X POST http://localhost:8080/jobs \
  -H "Content-Type: application/json" \
  -d '{"payload": {"title": "My First Job", "data": "test"}}'

# Check job status (replace {job_id} with actual ID from response)
curl http://localhost:8080/jobs/{job_id}

# Get download URL
curl http://localhost:8080/jobs/{job_id}/download

# Download the result file
curl "$(curl -s http://localhost:8080/jobs/{job_id}/download | jq -r .download_url)"

Local Testing

Test locally in 4 simple steps:

1. Install

pip install praisonai-svc

2. Create Service

praisonai-svc new my-service
cd my-service

3. Set Up Azure (Choose One)

Option A: Local Testing (No Azure Account)

# Install and start Azurite
npm install -g azurite
azurite --silent

# Use this in .env:
PRAISONAI_AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://127.0.0.1:10000/devstoreaccount1;QueueEndpoint=http://127.0.0.1:10001/devstoreaccount1;TableEndpoint=http://127.0.0.1:10002/devstoreaccount1;"

Option B: Real Azure

# Get from Azure Portal → Storage Account → Access Keys
PRAISONAI_AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=https;..."

4. Run & Test

# Start service
python app.py

# Test (in another terminal)
curl http://localhost:8080/health
curl -X POST http://localhost:8080/jobs \
  -H "Content-Type: application/json" \
  -d '{"payload": {"title": "Test"}}'

That's it! ✅

Architecture

┌─────────────┐
│  WordPress  │
│   Chatbot   │
└──────┬──────┘
       │ POST /jobs
       ▼
┌─────────────────────────────────────┐
│   Azure Container App (FastAPI)     │
│  ┌──────────┐      ┌──────────┐    │
│  │   API    │      │  Worker  │    │
│  └────┬─────┘      └─────┬────┘    │
└───────┼──────────────────┼──────────┘
        │                  │
        ▼                  ▼
   ┌─────────┐        ┌─────────┐
   │  Table  │        │  Queue  │
   │ Storage │        │ Storage │
   └─────────┘        └─────────┘
                           │
                           ▼
                      ┌─────────┐
                      │  Blob   │
                      │ Storage │
                      └─────────┘

API Endpoints

Method	Path	Description
POST	/jobs	Create new job
GET	/jobs/{id}	Get job status
GET	/jobs/{id}/download	Get fresh download URL
GET	/health	Health check

Configuration

All configuration via environment variables with PRAISONAI_ prefix:

# Required
PRAISONAI_AZURE_STORAGE_CONNECTION_STRING=...

# Optional
PRAISONAI_API_KEY=secret
PRAISONAI_CORS_ORIGINS=["https://example.com"]
PRAISONAI_MAX_JOB_DURATION_MINUTES=10
PRAISONAI_MAX_RETRY_COUNT=3

Deployment

Azure Container Apps

Quick deploy:

# Build and push image
docker build -t myregistry.azurecr.io/my-service:latest .
docker push myregistry.azurecr.io/my-service:latest

# Deploy to Azure Container Apps
az containerapp create \
  --name my-service \
  --resource-group my-rg \
  --environment my-env \
  --image myregistry.azurecr.io/my-service:latest \
  --target-port 8080 \
  --ingress external \
  --min-replicas 0 \
  --max-replicas 3

Security

Official Package

⚠️ The only official package is: praisonai-svc

Install via:

pip install praisonai-svc

Typosquatting Protection

We maintain a defensive package for common typo:

• praisonai-svcs (plural) → redirects to praisonai-svc

PyPI's built-in name similarity protection prevents other typosquatting attempts.

Report Security Issues

GitHub Issues: https://github.com/MervinPraison/PraisonAI-SVC/issues

Development

# Clone repository
git clone https://github.com/MervinPraison/PraisonAI-SVC.git
cd praisonai-svc

# Install with dev dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/
ruff check src/ --fix

# Type check
mypy src/

Version Management

When incrementing the version, update these files:

1. pyproject.toml - Line 3: version = "X.Y.Z"
2. src/praisonai_svc/cli.py - Line 7: @click.version_option(version="X.Y.Z")
3. tests/AUTOTESTING.md - Lines 445-447: Update version references

Version numbering (Semantic Versioning):

• Patch (X.Y.Z) - Bug fixes only: 1.2.0 → 1.2.1
• Minor (X.Y.0) - New features, backward compatible: 1.2.0 → 1.3.0
• Major (X.0.0) - Breaking changes: 1.2.0 → 2.0.0

Example:

# For a new feature (minor version bump)
# 1. Update pyproject.toml: version = "1.3.0"
# 2. Update cli.py: @click.version_option(version="1.3.0")
# 3. Update AUTOTESTING.md: Framework Version: 1.3.0
# 4. Commit and tag
git commit -am "Bump version to 1.3.0"
git tag v1.3.0

Project Structure

praisonai-svc/
├── src/praisonai_svc/          # Main package
│   ├── __init__.py             # Package exports
│   ├── app.py                  # ServiceApp class
│   ├── worker.py               # Worker with exponential backoff
│   ├── cli.py                  # CLI commands
│   ├── models/                 # Data models
│   └── azure/                  # Azure integrations
├── tests/                      # Test suite (20 tests)
├── examples/                   # Example services
├── pyproject.toml              # Package configuration
├── Dockerfile                  # Container image
└── LICENSE                     # MIT License

Key Features

Core Framework

• ServiceApp class - FastAPI app factory
• @app.job decorator - Simple handler registration
• Automatic API generation - 4 endpoints created automatically
• CORS middleware - Configurable cross-origin support
• Idempotency - SHA256 JobHash prevents duplicate processing

Azure Integration

• Blob Storage - File storage with retry logic (3 attempts)
• Queue Storage - Job queue with poison queue for failures
• Table Storage - Job state tracking with retry logic
• SAS URLs - On-demand secure download links (1h expiry)

Reliability

• Exponential backoff - Worker polling (1s → 30s)
• Retry logic - Max 3 attempts before poison queue
• Timeout detection - 10 minute job timeout
• Error handling - Comprehensive error messages

CLI Commands

praisonai-svc new <name>

Purpose: Create a new service from template

Usage:

praisonai-svc new my-service
# or with package integration
praisonai-svc new my-service --package praisonaippt

What it does:

1. Creates a new directory with your service name
2. Generates app.py with ServiceApp boilerplate
3. Creates .env.example with required configuration
4. Creates README.md with setup instructions
5. Generates pyproject.toml with dependencies

Output:

✅ Service created: my-service/
   ├── app.py
   ├── .env.example
   ├── README.md
   └── pyproject.toml

Next steps after creation:

cd my-service
cp .env.example .env
# Edit .env with your Azure credentials
# Edit app.py to implement your logic

---

praisonai-svc run

Purpose: Run the service locally for development

Usage:

cd my-service
praisonai-svc run

What it does:

1. Loads environment variables from .env
2. Starts FastAPI server on http://localhost:8080
3. Starts worker process for job processing
4. Enables hot-reload for development

Output:

INFO:     Started server process [12345]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8080

Test it:

# Health check
curl http://localhost:8080/health

# Create a job
curl -X POST http://localhost:8080/jobs \
  -H "Content-Type: application/json" \
  -d '{"payload": {"title": "Test"}}'

---

praisonai-svc deploy

Purpose: Deploy service to Azure Container Apps

Usage:

cd my-service
praisonai-svc deploy

What it does:

1. Validates Azure CLI is installed and authenticated
2. Builds Docker image from your service
3. Pushes image to Azure Container Registry
4. Creates/updates Azure Container App
5. Configures environment variables
6. Sets up scaling rules (min 0, max 3 replicas)

Prerequisites:

• Azure CLI installed: brew install azure-cli
• Logged in: az login
• Resource group and container registry created

Output:

🔨 Building Docker image...
✅ Image built: myregistry.azurecr.io/my-service:latest

📤 Pushing to Azure Container Registry...
✅ Image pushed

🚀 Deploying to Azure Container Apps...
✅ Deployed: https://my-service.azurecontainerapps.io

📊 Service URL: https://my-service.azurecontainerapps.io

What happens:

• Service is deployed with scale-to-zero (no cost when idle)
• Auto-scales based on HTTP requests (0-3 replicas)
• Environment variables from .env are configured
• HTTPS endpoint is automatically provisioned

---

praisonai-svc logs

Purpose: View real-time logs from deployed service

Usage:

cd my-service
praisonai-svc logs

# Follow logs (like tail -f)
praisonai-svc logs --follow

# Show last 100 lines
praisonai-svc logs --tail 100

What it does:

1. Connects to Azure Container Apps
2. Streams application logs in real-time
3. Shows both API and worker logs
4. Displays timestamps and log levels

Output:

2025-11-04 21:00:15 INFO  [API] Started server
2025-11-04 21:00:20 INFO  [API] POST /jobs - 201 Created
2025-11-04 21:00:21 INFO  [Worker] Processing job abc123
2025-11-04 21:00:25 INFO  [Worker] Job abc123 completed
2025-11-04 21:00:26 INFO  [API] GET /jobs/abc123 - 200 OK

Useful for:

• Debugging production issues
• Monitoring job processing
• Tracking API requests
• Identifying errors

Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=praisonai_svc

# Run specific test file
pytest tests/test_integration.py -v

Test Results: 20/20 tests passing ✅

Examples

See examples/ directory:

• examples/ppt-service/ - PowerPoint generation example

Documentation

• PRD - Complete product requirements
• GitHub: https://github.com/MervinPraison/PraisonAI-SVC
• Issues: https://github.com/MervinPraison/PraisonAI-SVC/issues

License

MIT License - see LICENSE file

Contributing

Contributions welcome! Please open an issue or pull request on GitHub.

Support

• GitHub Issues: https://github.com/MervinPraison/PraisonAI-SVC/issues
• Documentation: https://mervinpraison.github.io/PraisonAI-SVC
• Website: https://praison.ai