Data Schemas

Create and manage reusable schemas that define what data to extract

Schemas define the structure of data you want to extract from websites. They specify field names, data types, and descriptions. Schemas can be reused across multiple workflows.

For example, if you need to extract store locations from 5 different websites, you can create one schema and use it for all of them.

Managing Schemas

In UI

Create a New Schema

1. Go to My Schemas and click ‘Create Schema’
2. Choose how to start:
   • Create your own - Define your own data structure with custom fields
   • Copy from an existing workflow - Use the schema from one of your workflows as a starting point
   • Copy from an existing schema - Duplicate and modify one of your existing schemas
3. Add, remove, or modify fields to match your data extraction needs
4. Save your schema to use it in future workflows

Using Schemas in Workflows

When you create a new workflow, you can select one of your saved schemas to ensure consistent data extraction across different sources.

This saves time and ensures your data always follows the same structure, making it easier to work with your extracted information.

Learn more about UI workflow creation →

In SDK/API

Define Schemas Inline

Define schemas directly in your workflow creation code:

const workflow = await client
  .extract({
    urls: ['https://example.com'],
    extraction: builder => builder
      .entity('Product')
      .field('name', 'Product name', 'STRING', { example: 'Laptop' })
      .field('price', 'Price in USD', 'MONEY')
      .field('inStock', 'Availability', 'BOOLEAN')
  })
  .create();

Create Reusable Schemas

Create and manage schemas separately for reuse:

// Create a schema
const schema = await client.schemas.create({
  entity: 'Product',
  fields: [
    { name: 'name', description: 'Product name', dataType: 'STRING' },
    { name: 'price', description: 'Price', dataType: 'MONEY' },
    { name: 'inStock', description: 'In stock', dataType: 'BOOLEAN' }
  ]
});

// Use schema in workflow
const workflow = await client
  .extract({
    urls: ['https://example.com'],
    schemaId: schema.id
  })
  .create();

Learn more about SDK schema management →

Data Types

When defining schemas, you specify the data type for each field to ensure accurate extraction and validation. Kadoa supports the following data types:

Data Type	Description	Example Use Cases
STRING	String/text content	Product names, descriptions, article headlines
NUMBER	Numeric values (integers, decimals)	Quantities, ratings, scores, counts
BOOLEAN	True/false values	Availability status, feature flags, yes/no indicators
DATE	Date values	Publication dates, deadlines, event dates
DATETIME	Date and time values	Timestamps, scheduled times, last updated
MONEY	Currency and monetary values	Prices, costs, revenue, discounts
IMAGE	Image URLs and references	Product photos, thumbnails, profile pictures
LINK	URLs and hyperlinks	Product pages, external links, social media
OBJECT	Nested/complex JSON structures	Structured metadata, complex configurations
ARRAY	Lists/arrays of values	Tags, categories, multiple images, feature lists

Choose the appropriate data type to ensure your data is extracted and validated correctly.

Data Type Formats

Some data types return structured values:

Data Type	Format	Example
MONEY	{"amount": number, "currencyCode": string}	$124.50 → {"amount": 12450, "currencyCode": "USD"}

The amount field is always in the smallest currency unit (e.g., cents for USD, pence for GBP).

Special Field Types

Beyond regular data fields, Kadoa supports special field types for advanced use cases:

Classification Fields

Automatically categorize content into predefined labels. Useful for:

• Sentiment analysis (Positive/Negative/Neutral)
• Content categorization (Technology/Business/Sports)
• Priority classification (High/Medium/Low)

Learn more about classification in the SDK →

Metadata Fields (Raw Content)

Extract raw page content in different formats:

• HTML - Raw HTML source code
• MARKDOWN - Markdown formatted content
• PAGE_URL - Page URL

Learn more about metadata fields in the SDK →

Need help creating a custom schema? Contact our support team for assistance.