Input Parameters

Input parameters define the data that your tasks will process. Understanding how to configure them correctly is crucial for building effective workflows.

Parameter Types

TypeDescriptionExample
STRINGText data”Hello World”
INTEGERWhole numbers42
FLOATDecimal numbers3.14
BOOLEANTrue/false valuestrue

Complex Type Configuration

Array Parameters

⚠️ Important Rules:
  • Maximum 2D arrays allowed
  • Inner array elements can be basic types or objects
  • No 3D or deeper arrays

Object Parameters

⚠️ Important Rules:
  • Properties must use only:
    • STRING
    • INTEGER
    • FLOAT
    • BOOLEAN
  • No nested objects allowed
  • Each property needs name, description, type, and required fields

Parameter Sources

Available Sources

  1. Task Config
    • Set during task setup
    • Fixed values
    • Template values
  2. System Generated
    • Created during execution
    • Dynamic values
    • No manual input needed
  3. Human Input
    • Provided during workflow
    • User interaction required
    • Interactive forms

Data Sources

Memory Storage

  • Default storage method
  • For regular data volumes
  • No special configuration needed

Data Lake Storage

  • For large datasets
  • Persistent storage
  • Requires data_lake_id

Configuration Examples

String Parameter

{
  "name": "username",
  "description": "User's login name",
  "type": "STRING",
  "required": true,
  "source": "task_config",
  "value": "john_doe"
}

Number Parameter

{
  "name": "age",
  "description": "User's age",
  "type": "INTEGER",
  "required": true,
  "source": "task_config",
  "value": 25
}

Validation Rules

  1. Object Properties
    • Only basic types allowed (string, integer, float, boolean)
    • All fields required (name, description, type, required)
    • No nested objects
  2. Array Items
    • Maximum 2D arrays
    • Basic types or objects for elements
    • Proper null values for unused fields
  3. Type Consistency
    • Must follow exact schema
    • No additional fields
    • Proper null handling

Best Practices

  1. Naming Conventions
    • Use clear, descriptive names
    • Follow consistent conventions
    • Indicate purpose in name
  2. Documentation
    • Be specific and clear
    • Include format requirements
    • Note any dependencies
  3. Type Selection
    • Use simplest type possible
    • Consider data flow requirements
    • Plan for scale
  4. Validation
    • Set appropriate required flags
    • Include format constraints
    • Consider edge cases

Common Issues and Solutions

IssueSolution
Type mismatchVerify data type matches configuration
Missing required valueCheck source configuration
Invalid formatReview validation rules
Data source errorVerify data lake configuration
Invalid object propertyUse only allowed basic types
Array depth exceededRestructure to 2D maximum