Checker Tools

Data validation and checking tools for quality assurance

Overview

Checker Tools provide comprehensive data validation and quality assurance capabilities. These tools help ensure data integrity, compliance with business rules, and automated quality control in your workflows.

Key Features

CHECKER_CHECK_BY_JSON

• Uses an LLM to check text against rules defined as a JSON array
• Each rule can include a name, description, optional references, and optional few-shot examples
• Detailed validation results with reasons
• Inputs: text (text to check), task (task name), description (optional task description), rules (JSON array of rules)

CHECKLIST

• Uses an LLM to evaluate whether the input text complies with each rule defined in a CSV file
• CSV columns: name, description, optional references (semicolon-separated), optional examples (JSON)
• Structured validation reporting
• Inputs: text (text to check), task (task name), description (optional task description), rules (URL of the CSV rules file)

JINBA_MODULES_CHECKER_V2

• Advanced validation using JSON rule files
• Enhanced rule processing
• Improved performance and accuracy
• Version 2 with additional features

Authentication

No authentication required for checker tools.

Example: Document Validation

- id: validate_document
  name: validate_document
  tool: CHECKER_CHECK_BY_JSON
  input:
    - name: text
      value: "{{steps.extract_document.result.content}}"
    - name: task
      value: "Contract Compliance Check"
    - name: description
      value: "Validate contract document against legal requirements"
    - name: rules
      value: |
        [
          {
            "name": "Must contain signature section",
            "description": "The document must contain a signature section (e.g. 'signature', 'sign here', 'executed by')."
          },
          {
            "name": "Must include termination clause",
            "description": "The document must include a termination clause describing how the agreement ends or expires."
          },
          {
            "name": "Should specify payment terms",
            "description": "The document should specify payment terms such as invoicing, billing, or due dates."
          }
        ]

Example: Data Quality Validation

- id: extract_data
  name: extract_data
  tool: EXCEL_GET_ROWS
  input:
    - name: file_url
      value: "{{steps.input_file.result.file_url}}"
    - name: range
      value: "A1:E100"

- id: validate_data_quality
  name: validate_data_quality
  tool: CHECKER_CHECK_BY_JSON
  input:
    - name: text
      value: "{{steps.extract_data.result.content | join('\n')}}"
    - name: task
      value: "Customer Data Validation"
    - name: rules
      value: |
        [
          {
            "name": "Email format validation",
            "description": "Every email address must follow a valid email format (e.g. [e-mail])."
          },
          {
            "name": "Phone number format",
            "description": "Every phone number must be a valid international phone number."
          },
          {
            "name": "Complete address information",
            "description": "Each record should contain complete address information (street, city, etc.)."
          }
        ]

- id: process_validation_results
  name: process_validation_results
  tool: PYTHON_SANDBOX_RUN
  input:
    - name: code
      value: |
        import json
        
        validation_results = {{steps.validate_data_quality.result}}
        
        # Count validation status
        accepted = sum(1 for r in validation_results if r['status'] == 'accepted')
        rejected = sum(1 for r in validation_results if r['status'] == 'rejected')
        pending = sum(1 for r in validation_results if r['status'] == 'pending')
        
        print(f"Validation Summary:")
        print(f"✅ Accepted: {accepted}")
        print(f"❌ Rejected: {rejected}")
        print(f"⏳ Pending: {pending}")
        
        # List rejected items with reasons
        if rejected > 0:
            print("\nRejected Items:")
            for result in validation_results:
                if result['status'] == 'rejected':
                    print(f"- {result['rule']}: {result['reason']}")

Example: Advanced File Validation

- id: upload_rules_file
  name: upload_rules_file
  tool: INPUT_FILE
  input:
    - name: description
      value: "Upload validation rules CSV file"

- id: validate_with_checklist
  name: validate_with_checklist
  tool: CHECKLIST
  input:
    - name: text
      value: "{{steps.input_data.result}}"
    - name: task
      value: "Batch Data Validation"
    - name: rules
      value: "{{steps.upload_rules_file.result}}"

- id: advanced_validation
  name: advanced_validation
  tool: JINBA_MODULES_CHECKER_V2
  input:
    - name: target_file
      value: "{{steps.input_data.result.file_url}}"
    - name: task
      value: "Advanced Data Validation"
    - name: description
      value: "Comprehensive validation using enhanced checker v2"
    - name: rules
      value: |
        [
          {
            "uniqueId": "email_check",
            "name": "Email format validation",
            "description": "Every email address must follow a valid email format (e.g. [e-mail])."
          },
          {
            "uniqueId": "age_check",
            "name": "Age range validation",
            "description": "Every age value must be a number between 18 and 120."
          },
          {
            "uniqueId": "country_check",
            "name": "Country code validation",
            "description": "Every country code must be one of US, UK, CA, AU, or JP."
          }
        ]
    - name: additionalDataSchema
      value: |
        {
          "extractedData": {
            "type": "object",
            "properties": {
              "email": {"type": "string"},
              "age": {"type": "number"},
              "country": {"type": "string"}
            }
          }
        }

Validation Rule Types

Pattern-based Rules

• Regex patterns: Use regular expressions for complex validation
• Text matching: Simple text presence or absence checks
• Format validation: Email, phone, URL, date formats

Structural Rules

• Required fields: Ensure mandatory data is present
• Data types: Validate numeric, date, boolean data
• Range validation: Min/max values, length constraints

Business Rules

• Custom logic: Complex business rule validation
• Cross-field validation: Rules that depend on multiple fields
• Conditional rules: Rules that apply under certain conditions

Validation Results

Each validation returns an array of structured results:

{
  "rule": "Email format validation",
  "status": "accepted|rejected|pending",
  "range": [0, 120],
  "reason": "Detailed explanation of validation result"
}

range is an array of numbers indicating the character range the result applies to.

Use Cases

• Data Quality Assurance: Validate imported data quality
• Compliance Checking: Ensure documents meet regulatory requirements
• Form Validation: Validate user-submitted forms and applications
• Content Moderation: Check content against community guidelines
• Business Rule Enforcement: Ensure data meets business criteria
• Import Validation: Validate data before importing into systems
• Document Review: Automated document compliance checking