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Async Jobs Deployment

Deploy the async jobs server for production-grade agent and recipe execution with persistence, webhooks, and streaming.

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Overview

The async jobs server provides:
  • HTTP API for job submission and management
  • Persistent job storage
  • Webhook notifications
  • SSE streaming for real-time progress
  • Idempotency support

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Quick Start

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Start the Server

# Development
python -m uvicorn praisonai.jobs.server:create_app --port 8005 --factory

# Production with multiple workers
python -m uvicorn praisonai.jobs.server:create_app \
    --host 0.0.0.0 \
    --port 8005 \
    --workers 4 \
    --factory

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Submit a Job

# Submit with CLI
praisonai run submit "Analyze data" --recipe analyzer --api-url http://localhost:8005

# Submit with Python
from praisonai import recipe

job = recipe.submit_job(
    "my-recipe",
    input={"query": "What is AI?"},
    api_url="http://localhost:8005",
)

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Docker Deployment

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Dockerfile

FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy application
COPY . .

# Expose port
EXPOSE 8005

# Set environment variables
ENV OPENAI_API_KEY=${OPENAI_API_KEY}
ENV PRAISONAI_JOBS_PORT=8005
ENV PRAISONAI_JOBS_HOST=0.0.0.0

# Run server
CMD ["python", "-m", "uvicorn", "praisonai.jobs.server:create_app", "--host", "0.0.0.0", "--port", "8005", "--factory"]

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Docker Compose

version: '3.8'

services:
  jobs-server:
    build: .
    ports:
      - "8005:8005"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - PRAISONAI_JOBS_MAX_CONCURRENT=10
      - PRAISONAI_JOBS_DEFAULT_TIMEOUT=3600
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8005/health"]
      interval: 30s
      timeout: 10s
      retries: 3
    restart: unless-stopped

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Configuration

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Environment Variables

VariableDefaultDescription
PRAISONAI_JOBS_PORT8005Server port
PRAISONAI_JOBS_HOST127.0.0.1Server host
PRAISONAI_JOBS_MAX_CONCURRENT10Max concurrent jobs
PRAISONAI_JOBS_DEFAULT_TIMEOUT3600Default timeout (seconds)

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TEMPLATE.yaml Runtime Block

Configure job defaults in your recipe: 
runtime:
  job:
    enabled: true
    timeout_sec: 3600
    webhook_url: "${WEBHOOK_URL}"
    idempotency_scope: "session"
    events:
      - completed
      - failed

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API Endpoints

EndpointMethodDescription
/api/v1/runsPOSTSubmit a new job
/api/v1/runsGETList all jobs
/api/v1/runs/{id}GETGet job status
/api/v1/runs/{id}/resultGETGet job result
/api/v1/runs/{id}/streamGETStream job progress (SSE)
/api/v1/runs/{id}/cancelPOSTCancel a job
/api/v1/runs/{id}DELETEDelete a job
/healthGETHealth check
/statsGETServer statistics

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Production Considerations

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Load Balancing

For high availability, deploy multiple instances behind a load balancer: 
upstream jobs_servers {
    server jobs1:8005;
    server jobs2:8005;
    server jobs3:8005;
}

server {
    listen 80;
    
    location / {
        proxy_pass http://jobs_servers;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
    }
}

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Webhooks

Configure webhooks for job completion notifications: 
job = recipe.submit_job(
    "my-recipe",
    webhook_url="https://your-server.com/webhook",
)
Webhook payload: 
{
  "job_id": "run_abc123",
  "status": "succeeded",
  "result": {"output": "..."},
  "completed_at": "2024-01-15T10:30:00Z",
  "duration_seconds": 45.2
}

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Idempotency

Prevent duplicate job submissions: 
praisonai run submit "Task" --idempotency-key order-123 --idempotency-scope global

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Monitoring

# Check server health
curl http://localhost:8005/health

# Get server stats
curl http://localhost:8005/stats

# List running jobs
praisonai run list --status running --json

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Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: praisonai-jobs
spec:
  replicas: 3
  selector:
    matchLabels:
      app: praisonai-jobs
  template:
    metadata:
      labels:
        app: praisonai-jobs
    spec:
      containers:
      - name: jobs-server
        image: praisonai/jobs-server:latest
        ports:
        - containerPort: 8005
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: praisonai-secrets
              key: openai-api-key
        - name: PRAISONAI_JOBS_MAX_CONCURRENT
          value: "10"
        livenessProbe:
          httpGet:
            path: /health
            port: 8005
          initialDelaySeconds: 10
          periodSeconds: 30
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "2000m"
---
apiVersion: v1
kind: Service
metadata:
  name: praisonai-jobs
spec:
  selector:
    app: praisonai-jobs
  ports:
  - port: 8005
    targetPort: 8005
  type: LoadBalancer

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See Also