Templates are a core part of the document extraction process. They define what information the system should extract from a document and how that information should be structured in the final output. A template is essentially a list of fields written in natural language that tells the system what to look for (for example, invoice number or total amount).

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Field Names

Field names are required for every piece of information you want to extract from a document. When defining field names, simply think about the data you want to capture. For example, when extracting data from an invoice, you might start with:

• Invoice number
• Invoice date
• Invoice total

Naming Rules:

• Field names cannot contain spaces.
• If you type a field name with spaces, the system will automatically replace them with underscores.
  • Example: Invoice Number → Invoice_Number

Field names should be clear and descriptive, as they become the keys in the extracted output.

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Data Types

Data types tell the system how to interpret, format, and structure the extracted information. Choosing the correct data type improves accuracy and enables advanced features such as line items and nested fields. Supported Data Types:

List of Fields (Line Items)

The List of Fields data type is used when the same set of information repeats multiple times in a document, such as invoice line items.When you change a field’s data type to List of Fields:

• A first subfield is automatically created
• An Add Subfield button appears

Think of subfields as column names for each line item. For example, an invoice line item list might include:

• Item description
• Quantity
• Unit price
• Line total

Nested Line Items:

• A subfield can also be set to List of Fields, allowing line items within line items.
• The system supports up to 2 levels of depth for nested lists.

Section / Group

Sections / Groups allow you to organize related fields under a single parent field.They work similarly to List of Fields, with one key difference:

• Subfields in a section do not repeat
• They simply store child fields that logically belong together

Example Use Cases:

• Grouping billing address fields
• Grouping payment details
• Grouping vendor or customer information

Nesting Rules:

• A Section / Group can contain:
  • Text fields
  • A List of Fields
  • Another Section / Group
• You are limited to 2 levels of nesting in total.

Choose from a List

The Choose from a List data type restricts the extracted value to a predefined set of options. This is effectively an enumeration.Use this when the output should always be one of a known set of values, such as:

• Payment method: Credit Card, Bank Transfer, Cash
• Document type: Invoice, Receipt, Purchase Order

This helps enforce consistency and prevents unexpected outputs.

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Description

The Description field helps the system locate the correct information in the document when it is unclear or poorly labeled. Use the description to explain:

• Where the information appears in the document
• What the information looks like
• How it is labeled (or mislabeled)

Guidelines:

• Descriptions are optional
• It is recommended to leave this empty unless the system struggles
• Overusing descriptions can reduce flexibility

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Rules

Rules describe what the system should do with the extracted information before returning it. While descriptions answer:

“Help me find this information”

Rules answer:

“What should I do with this information once I find it?”

Rules are written in natural language and can be used to:

• Transform values
• Split combined fields
• Normalize formats
• Apply logic (e.g., when to set a boolean value)

**Example Use Case:**xample Use Case In one example, a document contained a single column labeled Description that included both:

• The item name
• The item dimensions

Using Rules, the system was instructed to:

• Separate the item description from the dimensions
• Store each value in its own field during extraction