# Fetch ML Configuration Example for PostgreSQL
# This example shows how to configure Fetch ML to use PostgreSQL as the database

auth:
  enabled: true
  apikeys:
    admin:
      hash: "5e884898da28047151d0e56f8dc6292773603d0d6aabbdd5f8b6c8b0b4f0b8e3" # "password"
      admin: true
      roles: ["admin"]

server:
  address: ":9101"
  tls:
    enabled: false

database:
  type: "postgres"
  host: "localhost"
  port: 5432
  username: "fetchml"
  password: "your_password_here"
  database: "fetchml"
  # Alternatively, you can use a full connection string:
  # connection: "postgres://fetchml:your_password_here@localhost:5432/fetchml?sslmode=disable"

redis:
  host: "localhost"
  port: 6379
  password: ""
  db: 0
  pool_size: 10
  max_retries: 3

logging:
  level: "info"
  console: true
  format: "text"

security:
  secret_key: "your-secret-key-here-at-least-16-characters"
  jwt_expiry: "24h"
  rate_limit:
    enabled: false
    requests_per_minute: 60
    burst_size: 10

containers:
  runtime: "podman"
  registry: "docker.io"
  pull_policy: "missing"
  resources:
    cpu_limit: "2"
    memory_limit: "4Gi"
    gpu_limit: 1

storage:
  data_path: "data"
  results_path: "results"
  temp_path: "/tmp/fetch_ml"
  cleanup:
    enabled: true
    max_age_hours: 168
    max_size_gb: 10