Validations

Validations allow you to enforce rules on extracted data to ensure quality and consistency. TableFlow supports various validation types that can be applied to both template fields and table columns. Any data that fails validation will be flagged in the review interface, allowing for manual correction before further processing.

How Validations Work

Validations are defined as an array on template fields or table columns:

"validations": [
  {
    "validate": "not_blank",
    "message": "This field is required",
    "severity": "error"
  },
  {
    "validate": "regex",
    "options": "^INV-\\d{4}$",
    "message": "Invoice number must follow format INV-XXXX",
    "severity": "error"
  }
]

Each validation has the following properties:

validate (required): The type of validation rule
options: Additional configuration for the validation rule (varies by type)
message: Custom error message to display on validation failure
severity: The severity level of the validation failure (“error”, “warn”, or “info”)

Validation Types

Universal Validations

These validations can be applied to any data type.

Not Blank

Ensures that the field or cell contains a non-blank value.

{
  "validate": "not_blank",
  "message": "This field is required"
}

String Validations

These validations can only be applied to string data types.

List

Ensures the value matches one of the items in a predefined list. Comparisons are case-insensitive.

{
  "validate": "list",
  "options": ["Small", "Medium", "Large"],
  "message": "Size must be one of the standard options"
}

Email

Ensures the value is a valid email address.

{
  "validate": "email",
  "message": "Please enter a valid email address"
}

Phone

Ensures the value is a valid phone number.

{
  "validate": "phone",
  "message": "Please enter a valid phone number"
}

Length

Validates the length of text content.

{
  "validate": "length",
  "options": {
    "min": 4,
    "max": 16
  },
  "message": "Value must be between 4 and 16 characters"
}

You can specify only min or max:

{
  "validate": "length",
  "options": {
    "min": 8
  },
  "message": "Value must be at least 8 characters"
}

Regex

Ensures the value matches a specific regular expression pattern.

{
  "validate": "regex",
  "options": "^[A-Za-z0-9-]+$",
  "message": "Only alphanumeric characters and hyphens are allowed"
}

Number Validations

These validations can only be applied to number data types.

Range

Validates that the number falls within a specific range.

{
  "validate": "range",
  "options": {
    "min": 0,
    "max": 100
  },
  "message": "Value must be between 0 and 100"
}

You can specify only min or max:

{
  "validate": "range",
  "options": {
    "min": 0
  },
  "message": "Value must be greater than or equal to 0"
}

Decimal

Validates the number of decimal places.

{
  "validate": "decimal",
  "options": {
    "places": 2
  },
  "message": "Value must have exactly 2 decimal places"
}

Date Validations

These validations can only be applied to date data types.

Date Range

Validates that a date falls within a specific range.

{
  "validate": "date_range",
  "options": {
    "min": "2023-01-01",
    "max": "2023-12-31"
  },
  "message": "Date must be in 2023"
}

Validation Severity Levels

TableFlow supports three severity levels for validations:

error: Prevents extraction data from being used until fixed
warning: Allows data to be used but indicates potential issues
info: Provides informational feedback without blocking

{
  "validate": "not_blank",
  "message": "This field is required",
  "severity": "error"
}

Combining Multiple Validations

You can apply multiple validation rules to the same field or column. All validations must pass for the data to be considered valid.

"validations": [
  {
    "validate": "not_blank",
    "message": "This field is required"
  },
  {
    "validate": "regex",
    "options": "^[A-Z]\\d{5}$",
    "message": "Product code must start with a capital letter followed by 5 digits"
  }
]

How Validations Affect Extraction

During the document extraction process:

Data is extracted from the document based on the template
Each extracted value is checked against applicable validations
Values that fail validation are flagged for review
The review interface highlights validation errors with their messages
Users can correct the data before finalizing the extraction

Example Validation Scenarios

Invoice Number Validation

{
  "name": "Invoice Number",
  "key": "invoice_number",
  "data_type": "string",
  "validations": [
    {
      "validate": "not_blank",
      "message": "Invoice number is required"
    },
    {
      "validate": "regex",
      "options": "^INV-\\d{4}$",
      "message": "Invoice number must follow format INV-XXXX"
    }
  ]
}

Amount Validation

{
  "name": "Total Amount",
  "key": "total_amount",
  "data_type": "number",
  "validations": [
    {
      "validate": "not_blank",
      "message": "Total amount is required"
    },
    {
      "validate": "range",
      "options": {
        "min": 0
      },
      "message": "Total amount must be positive"
    },
    {
      "validate": "decimal",
      "options": {
        "places": 2
      },
      "message": "Total amount must have exactly 2 decimal places"
    }
  ]
}

Best Practices

For optimal validation results:

Start Simple - Begin with basic validations and add complexity as needed
Use Clear Messages - Write clear, descriptive validation messages
Combine Validations - Use multiple validation types for complex rules
Set Appropriate Severity - Use error for critical validations, warnings for less critical issues
Test Thoroughly - Verify validations work with real-world documents

Next Steps

Learn how to use webhooks to integrate extracted data with your application.

Welcome

Core Concepts

Features

Integrations

How Validations Work

Validation Types

Universal Validations

Not Blank

String Validations

List

Email

Phone

Length

Regex

Number Validations

Range

Decimal

Date Validations

Date Range

Validation Severity Levels

Combining Multiple Validations

How Validations Affect Extraction

Example Validation Scenarios

Invoice Number Validation

Amount Validation

Best Practices

Next Steps

​How Validations Work

​Validation Types

​Universal Validations

​Not Blank

​String Validations

​List

​Email

​Phone

​Length

​Regex

​Number Validations

​Range

​Decimal

​Date Validations

​Date Range

​Validation Severity Levels

​Combining Multiple Validations

​How Validations Affect Extraction

​Example Validation Scenarios

​Invoice Number Validation

​Amount Validation

​Best Practices

​Next Steps

How Validations Work

Validation Types

Universal Validations

Not Blank

String Validations

List

Email

Phone

Length

Regex

Number Validations

Range

Decimal

Date Validations

Date Range

Validation Severity Levels

Combining Multiple Validations

How Validations Affect Extraction

Example Validation Scenarios

Invoice Number Validation

Amount Validation

Best Practices

Next Steps