test.yaml

# DialogChain Pipeline Configuration - Simple DSL
# This YAML format minimizes configuration complexity while maximizing flexibility

name: "smart_security_system"
version: "1.0.0"
description: "AI-powered security monitoring with multi-language processing"

# =============================================================================
# TRIGGERS - Various input sources with minimal configuration
# =============================================================================
triggers:
  - id: camera_feed
    type: http
    port: 8080
    path: /camera/frame
    enabled: true

  - id: motion_sensor
    type: mqtt
    broker: "mqtt://localhost:1883"
    topic: "sensors/motion"
    enabled: true

  - id: scheduled_check
    type: timer
    interval: 30000  # 30 seconds
    enabled: true

  - id: file_upload
    type: file_watch
    path: "/incoming/videos"
    pattern: "*.mp4"
    enabled: true

  - id: grpc_service
    type: grpc
    port: 9090
    service: "SecurityService"
    method: "ProcessFrame"
    enabled: false

# =============================================================================
# PROCESSORS - Multi-language processing with dependency management
# =============================================================================
processors:
  # Python AI/ML processing
  - id: object_detection
    type: python
    script: "scripts/yolo_detect.py"
    venv: "/opt/ml-env"
    parallel: true
    timeout: 5000
    retry: 2
    dependencies: []
    environment:
      CUDA_VISIBLE_DEVICES: "0"
      MODEL_PATH: "/models/yolov8n.pt"
      CONFIDENCE_THRESHOLD: "0.6"

  # Go-based high-performance analysis
  - id: threat_analysis
    type: go
    binary: "./analyzers/threat-detector"
    args: ["--confidence=0.7", "--realtime"]
    parallel: false
    timeout: 2000
    retry: 1
    dependencies: ["object_detection"]

  # Rust WASM for edge processing
  - id: edge_filter
    type: rust_wasm
    wasm: "./filters/noise_reduction.wasm"
    parallel: true
    timeout: 1000
    retry: 0
    dependencies: []

  # Node.js for API integrations
  - id: external_enrichment
    type: node
    script: "enrichers/geo_lookup.js"
    npm_env: "/opt/node-env"
    parallel: true
    timeout: 3000
    retry: 2
    dependencies: ["threat_analysis"]

  # Docker containerized processing
  - id: ml_inference
    type: docker
    image: "tensorflow/serving:latest"
    command: ["--model_name=security_model", "--port=8501"]
    parallel: true
    timeout: 4000
    retry: 1
    dependencies: ["edge_filter"]

  # Native function call (for maximum performance)
  - id: fast_transform
    type: native
    function: "image_preprocessing"
    parallel: true
    timeout: 500
    retry: 0
    dependencies: []

  # LLM integration for intelligent analysis
  - id: context_analysis
    type: llm
    model: "gpt-4"
    prompt: |
      Analyze this security event and determine threat level:
      Context: {{context}}
      Objects detected: {{objects}}
      Time: {{timestamp}}
      
      Respond with JSON: {"threat_level": 0-1, "description": "...", "actions": [...]}
    parallel: false
    timeout: 10000
    retry: 1
    dependencies: ["threat_analysis", "external_enrichment"]

# =============================================================================
# OUTPUTS - Flexible output destinations with batching and conditions
# =============================================================================
outputs:
  # Email alerts for high-priority threats
  - id: security_alert
    type: email
    smtp: "smtp://localhost:587"
    to: ["security@company.com", "admin@company.com"]
    condition: "threat_level > 0.8"
    template: "alert_template.html"

  # Real-time dashboard updates
  - id: dashboard_update
    type: websocket
    url: "ws://dashboard:3000/alerts"
    batch_size: 10
    condition: "threat_level > 0.3"

  # MQTT for IoT device communication
  - id: iot_response
    type: mqtt
    broker: "mqtt://localhost:1883"
    topic: "security/response"
    condition: "threat_level > 0.5"

  # Database logging for audit
  - id: audit_log
    type: database
    connection: "postgresql://user:pass@localhost/security"
    table: "security_events"
    batch_size: 100

  # File storage for evidence
  - id: evidence_storage
    type: file
    path: "/evidence/{{date}}/{{event_id}}"
    format: "json"
    condition: "threat_level > 0.6"

  # HTTP API callback
  - id: external_notification
    type: http
    url: "https://api.security-service.com/events"
    method: "POST"
    headers:
      Authorization: "Bearer {{API_TOKEN}}"
    condition: "threat_level > 0.9"

# =============================================================================
# SETTINGS - Global configuration for performance and security
# =============================================================================
settings:
  performance:
    max_concurrent: 10
    buffer_size: 1000
    worker_threads: 4

  monitoring:
    enabled: true
    metrics_port: 9100
    health_check_port: 8090
    log_level: "INFO"

  security:
    require_auth: true
    rate_limit: 1000  # requests per minute
    allowed_origins:
      - "https://dashboard.company.com"
      - "https://admin.company.com"
    encryption:
      tls_cert: "/certs/server.crt"
      tls_key: "/certs/server.key"

  deployment:
    runtime: "docker"
    replicas: 3
    resources:
      memory: "2Gi"
      cpu: "1000m"

# =============================================================================
# ADVANCED FEATURES
# =============================================================================

# Pipeline composition - reference other pipelines
includes:
  - "base_security.yaml"
  - "ml_models.yaml"

# Environment-specific overrides
environments:
  development:
    settings:
      log_level: "DEBUG"
      monitoring:
        enabled: false

  production:
    settings:
      security:
        rate_limit: 10000
      performance:
        max_concurrent: 50

# Feature flags for gradual rollout
features:
  enable_ml_inference: true
  enable_llm_analysis: false
  enable_edge_processing: true

# Custom extensions and plugins
extensions:
  - name: "prometheus_exporter"
    config:
      port: 9090
      namespace: "dialogchain"

  - name: "jaeger_tracing"
    config:
      endpoint: "http://jaeger:14268/api/traces"

# Data transformation templates
transforms:
  normalize_image:
    input: "raw_image"
    output: "normalized_image"
    function: "resize_and_normalize"
    params:
      width: 640
      height: 480
      normalize: true

# Conditional routing based on data content
routing:
  rules:
    - condition: "image_size > 1MB"
      processors: ["edge_filter", "object_detection"]
    - condition: "motion_detected == true"
      processors: ["object_detection", "threat_analysis", "context_analysis"]
    - default:
      processors: ["fast_transform", "object_detection"]