Input Parameters

Overview

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

Parameter Types

Basic Types

Type	Description	Example
STRING	Text data	”Hello World”
INTEGER	Whole numbers	42
FLOAT	Decimal numbers	3.14
BOOLEAN	True/false values	true

Complex Type Configuration

User Guide

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

User Guide

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

User Guide

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

Basic 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

Type 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

Issue	Solution
Type mismatch	Verify data type matches configuration
Missing required value	Check source configuration
Invalid format	Review validation rules
Data source error	Verify data lake configuration
Invalid object property	Use only allowed basic types
Array depth exceeded	Restructure to 2D maximum