flow.d2

# PaladinAI System Design & Query Flow

direction: down

title: {
  label: PaladinAI System Architecture & Query Flow
  near: top-center
  shape: text
  style.font-size: 28
}

# User Interfaces Layer
ui: User Interfaces {
  web: Web UI {
    icon: https://icons.terrastruct.com/dev%2Freact.svg
    shape: rectangle
  }
  cli: CLI Client {
    icon: https://icons.terrastruct.com/aws%2F_general%2Fclient.svg
    shape: rectangle
  }
  discord: Discord Bot {
    icon: https://icons.terrastruct.com/social%2F025-discord.svg
    shape: rectangle
  }
  api_client: API Client {
    shape: rectangle
  }
}

# API Gateway
gateway: API Gateway {
  fastapi: FastAPI Server {
    shape: hexagon
    style.fill: "#2E7D32"
  }
}

# Core Workflow Engine
workflow: Workflow Engine {
  start: Start Node {
    shape: circle
    style.fill: "#4CAF50"
  }
  
  guardrail: Guardrail Node {
    shape: diamond
    style.fill: "#FFC107"
  }
  
  categorize: Categorization {
    shape: diamond
    style.fill: "#2196F3"
  }
  
  flows: {
    query: Query Workflow {
      shape: rectangle
      style.fill: "#81C784"
    }
    action: Action Workflow {
      shape: rectangle
      style.fill: "#64B5F6"
    }
    incident: Incident Workflow {
      shape: rectangle
      style.fill: "#FFB74D"
    }
  }
  
  result: Result Node {
    shape: circle
    style.fill: "#9C27B0"
  }
}

# Data Collection Layer
collectors: Data Collection {
  prometheus: Prometheus Node {
    icon: https://icons.terrastruct.com/dev%2Fprometheus.svg
    shape: rectangle
  }
  loki: Loki Node {
    icon: https://icons.terrastruct.com/dev%2Fgrafana.svg
    shape: rectangle
  }
  alertmanager: Alertmanager Node {
    shape: rectangle
  }
}

# Intelligence Layer
intelligence: AI & Memory {
  llm: OpenAI GPT-4 {
    shape: hexagon
    style.fill: "#1976D2"
  }
  memory: Memory Service {
    shape: cylinder
    style.fill: "#4CAF50"
  }
  rag: RAG Engine {
    shape: cylinder
    style.fill: "#FF6F00"
  }
}

# Data Stores
storage: Data Layer {
  mongodb: MongoDB\n(Checkpoints) {
    shape: cylinder
    style.fill: "#4DB33D"
  }
  neo4j: Neo4j\n(Memory Graph) {
    shape: cylinder
    style.fill: "#018BFF"
  }
  qdrant: Qdrant\n(Vectors) {
    shape: cylinder
    style.fill: "#7B1FA2"
  }
}

# External Monitoring Stack
monitoring: Monitoring Stack {
  prom_server: Prometheus {
    shape: rectangle
    style.fill: "#E6522C"
  }
  loki_server: Loki {
    shape: rectangle
    style.fill: "#F46800"
  }
  grafana: Grafana {
    shape: rectangle
    style.fill: "#F46800"
  }
  alert_server: Alertmanager {
    shape: rectangle
    style.fill: "#E6522C"
  }
}

# Connections - User Flow
ui.web -> gateway.fastapi: "User Query"
ui.cli -> gateway.fastapi: "CLI Command"
ui.discord -> gateway.fastapi: "Discord Message"
ui.api_client -> gateway.fastapi: "API Request"

# Workflow Flow
gateway.fastapi -> workflow.start: "1. Initialize"
workflow.start -> workflow.guardrail: "2. Validate"
workflow.guardrail -> workflow.categorize: "3. Categorize"

workflow.categorize -> workflow.flows.query: "Simple Query"
workflow.categorize -> workflow.flows.action: "Analysis Task"
workflow.categorize -> workflow.flows.incident: "Investigation"

# Data Collection Flow
workflow.flows.query -> collectors.prometheus: "Fetch Metrics"
workflow.flows.action -> collectors.prometheus: "Collect Data"
workflow.flows.action -> collectors.loki: "Fetch Logs"
workflow.flows.incident -> collectors.prometheus: "Full Investigation"
workflow.flows.incident -> collectors.loki
workflow.flows.incident -> collectors.alertmanager: "Check Alerts"

# Intelligence Integration
workflow.guardrail <-> intelligence.memory: "Enhance Context"
workflow.flows.incident <-> intelligence.rag: "Search Docs"
collectors.prometheus -> intelligence.llm: "Analyze"
collectors.loki -> intelligence.llm: "Process"
collectors.alertmanager -> intelligence.llm: "Interpret"

# Result Generation
intelligence.llm -> workflow.result: "Generate Response"
workflow.result -> gateway.fastapi: "Final Result"

# Data Persistence
gateway.fastapi <-> storage.mongodb: "Checkpoints"
intelligence.memory <-> storage.neo4j: "Knowledge Graph"
intelligence.rag <-> storage.qdrant: "Embeddings"

# External Connections
collectors.prometheus <-> monitoring.prom_server: "PromQL"
collectors.loki <-> monitoring.loki_server: "LogQL"
collectors.alertmanager <-> monitoring.alert_server: "Alerts API"

# Legend
legend: Legend {
  style.fill: "#F5F5F5"
  style.stroke: "#9E9E9E"
  
  user_flow: "User Query Flow" {
    style.stroke-width: 3
    style.stroke: "#4CAF50"
  }
  
  data_flow: "Data Collection" {
    style.stroke-width: 3
    style.stroke: "#2196F3"
  }
  
  ai_flow: "AI Processing" {
    style.stroke-width: 3
    style.stroke: "#9C27B0"
  }
}

# Query Flow Example
example: {
  near: bottom-center
  label: |md
    ## Example Query Flow: "Why is the payment service slow?"
    1. User asks in natural language
    2. Guardrail validates & enhances with memory
    3. Categorized as "Incident" workflow
    4. Collects metrics, logs, alerts in parallel
    5. AI analyzes all data sources
    6. Generates root cause analysis
    7. Returns actionable response
  |
  shape: text
  style.font-size: 16
}