# Argo Events - EventSource for KFP and NATS integration
# Enables bidirectional triggering between Argo Workflows and Kubeflow Pipelines
---
apiVersion: argoproj.io/v1alpha1
kind: EventSource
metadata:
  name: kfp-events
  namespace: ai-ml
  labels:
    app.kubernetes.io/name: kfp-events
    app.kubernetes.io/part-of: ai-ml-pipelines
spec:
  service:
    ports:
      - name: webhook
        port: 12000
        targetPort: 12000
  # Webhook to receive KFP pipeline completion events
  webhook:
    kfp-completion:
      port: "12000"
      endpoint: /kfp/completion
      method: POST
    kfp-failure:
      port: "12000"
      endpoint: /kfp/failure
      method: POST
  # NATS for receiving pipeline trigger requests
  nats:
    pipeline-trigger:
      url: nats://nats.ai-ml.svc.cluster.local:4222
      subject: ai.pipeline.trigger
      jsonBody: true
    argo-trigger:
      url: nats://nats.ai-ml.svc.cluster.local:4222
      subject: ai.argo.trigger
      jsonBody: true
    kfp-trigger:
      url: nats://nats.ai-ml.svc.cluster.local:4222
      subject: ai.kfp.trigger
      jsonBody: true
---
# Sensor for handling KFP completion events
apiVersion: argoproj.io/v1alpha1
kind: Sensor
metadata:
  name: kfp-completion-sensor
  namespace: ai-ml
  labels:
    app.kubernetes.io/name: kfp-completion-sensor
    app.kubernetes.io/part-of: ai-ml-pipelines
spec:
  dependencies:
    - name: kfp-success
      eventSourceName: kfp-events
      eventName: kfp-completion
      filters:
        data:
          - path: body.status
            type: string
            value:
              - "SUCCEEDED"
    - name: kfp-failure
      eventSourceName: kfp-events
      eventName: kfp-failure
  triggers:
    # On KFP success, publish to NATS
    - template:
        name: notify-kfp-success
        nats:
          url: nats://nats.ai-ml.svc.cluster.local:4222
          subject: ai.pipeline.status.completed
          payload:
            - src:
                dependencyName: kfp-success
                dataKey: body.run_id
              dest: run_id
            - src:
                dependencyName: kfp-success
                dataKey: body.pipeline_name
              dest: pipeline_name
            - src:
                dependencyName: kfp-success
                dataKey: body.status
              dest: status
      retryStrategy:
        steps: 3
    # On KFP failure, trigger recovery workflow
    - template:
        name: kfp-failure-recovery
        k8s:
          operation: create
          source:
            resource:
              apiVersion: argoproj.io/v1alpha1
              kind: Workflow
              metadata:
                generateName: kfp-failure-handler-
                namespace: ai-ml
              spec:
                entrypoint: notify-failure
                arguments:
                  parameters:
                    - name: run-id
                    - name: pipeline-name
                    - name: error-message
                templates:
                  - name: notify-failure
                    inputs:
                      parameters:
                        - name: run-id
                        - name: pipeline-name
                        - name: error-message
                    script:
                      image: python:3.13-slim
                      command: [python]
                      source: |
                        import subprocess
                        import sys
                        subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "nats-py"])

                        import asyncio
                        import json
                        import nats

                        async def notify():
                            nc = await nats.connect("nats://nats.ai-ml.svc.cluster.local:4222")
                            await nc.publish(
                                "ai.pipeline.status.failed",
                                json.dumps({
                                    "run_id": "{{inputs.parameters.run-id}}",
                                    "pipeline_name": "{{inputs.parameters.pipeline-name}}",
                                    "error": "{{inputs.parameters.error-message}}",
                                    "source": "kubeflow"
                                }).encode()
                            )
                            await nc.close()

                        asyncio.run(notify())
          parameters:
            - src:
                dependencyName: kfp-failure
                dataKey: body.run_id
              dest: spec.arguments.parameters.0.value
            - src:
                dependencyName: kfp-failure
                dataKey: body.pipeline_name
              dest: spec.arguments.parameters.1.value
            - src:
                dependencyName: kfp-failure
                dataKey: body.error
              dest: spec.arguments.parameters.2.value
      retryStrategy:
        steps: 3
---
# Sensor for NATS-triggered Argo Workflows
apiVersion: argoproj.io/v1alpha1
kind: Sensor
metadata:
  name: nats-argo-sensor
  namespace: ai-ml
  labels:
    app.kubernetes.io/name: nats-argo-sensor
    app.kubernetes.io/part-of: ai-ml-pipelines
spec:
  dependencies:
    - name: argo-trigger
      eventSourceName: kfp-events
      eventName: argo-trigger
  triggers:
    - template:
        name: trigger-argo-workflow
        k8s:
          operation: create
          source:
            resource:
              apiVersion: argoproj.io/v1alpha1
              kind: Workflow
              metadata:
                generateName: nats-triggered-
                namespace: ai-ml
              spec:
                workflowTemplateRef:
                  name: placeholder
                arguments:
                  parameters: []
          parameters:
            - src:
                dependencyName: argo-trigger
                dataKey: body.template
              dest: spec.workflowTemplateRef.name
            - src:
                dependencyName: argo-trigger
                dataKey: body.parameters
              dest: spec.arguments.parameters
      retryStrategy:
        steps: 3
---
# Sensor for NATS-triggered KFP Pipelines
apiVersion: argoproj.io/v1alpha1
kind: Sensor
metadata:
  name: nats-kfp-sensor
  namespace: ai-ml
  labels:
    app.kubernetes.io/name: nats-kfp-sensor
    app.kubernetes.io/part-of: ai-ml-pipelines
spec:
  dependencies:
    - name: kfp-trigger
      eventSourceName: kfp-events
      eventName: kfp-trigger
  triggers:
    # Trigger KFP via Argo Workflow (uses kfp-trigger template)
    - template:
        name: trigger-kfp-via-argo
        k8s:
          operation: create
          source:
            resource:
              apiVersion: argoproj.io/v1alpha1
              kind: Workflow
              metadata:
                generateName: kfp-via-nats-
                namespace: ai-ml
              spec:
                workflowTemplateRef:
                  name: kfp-trigger
                arguments:
                  parameters:
                    - name: pipeline-id
                      value: ""
                    - name: pipeline-params
                      value: "{}"
                    - name: wait-for-completion
                      value: "true"
          parameters:
            - src:
                dependencyName: kfp-trigger
                dataKey: body.pipeline_id
              dest: spec.arguments.parameters.0.value
            - src:
                dependencyName: kfp-trigger
                dataKey: body.parameters
              dest: spec.arguments.parameters.1.value
              operation: "stringify"
            - src:
                dependencyName: kfp-trigger
                dataKey: body.wait
              dest: spec.arguments.parameters.2.value
              operation: "stringify"
      retryStrategy:
        steps: 3
---
# Service for the EventSource webhook
apiVersion: v1
kind: Service
metadata:
  name: kfp-events-webhook
  namespace: ai-ml
  labels:
    app.kubernetes.io/name: kfp-events
    app.kubernetes.io/part-of: ai-ml-pipelines
spec:
  selector:
    eventsource-name: kfp-events
  ports:
    - name: webhook
      port: 12000
      targetPort: 12000