Data Conversion Tools

Overview

Data Conversion Tools provide essential functionality for transforming data between different formats. These tools handle array-to-CSV conversion, image-to-PDF conversion, HTML-to-PDF/DOCX conversion, and other data transformation needs in your workflows.

Key Features

CONVERTER_ARRAY_TO_CSV

Convert 2D arrays to CSV format with automatic escaping
Handles special characters (quotes, commas, newlines)
Input: input (2D array) — Output: result (CSV string)
Compatible with spreadsheet applications

CONVERTER_IMAGE_TO_PDF

Convert images to PDF format
Support for TIFF, PNG, JPEG, GIF, and more
Input: file_link (array of image URLs, max 10 files)
Output: result (URL of the generated PDF)

CONVERTER_HTML_TO_PDF

Convert HTML content to a PDF file
Input: html_content (HTML string), filename (optional, without extension)
Output: result (signed URL to the created PDF file)

CONVERTER_HTML_TO_DOCX

Convert HTML content to a DOCX (Word) file
Input: html_content (HTML string), filename (optional, without extension)
Output: result (signed URL to the created DOCX file)

Authentication

No authentication required for conversion tools.

Example: Data Export to CSV

- id: fetch_data
  name: fetch_data
  tool: PYTHON_SANDBOX_RUN
  input:
    - name: code
      value: |
        # Sample data processing
        import json
        
        # Simulate fetching data from API or database
        raw_data = [
            {"name": "John Doe", "email": "john@example.com", "age": 30, "city": "New York"},
            {"name": "Jane Smith", "email": "jane@example.com", "age": 25, "city": "Los Angeles"},
            {"name": "Bob Johnson", "email": "bob@example.com", "age": 35, "city": "Chicago"}
        ]
        
        # Convert to 2D array format
        headers = ["Name", "Email", "Age", "City"]
        data_rows = []
        data_rows.append(headers)
        
        for item in raw_data:
            row = [item["name"], item["email"], str(item["age"]), item["city"]]
            data_rows.append(row)
        
        print(json.dumps(data_rows))

- id: convert_to_csv
  name: convert_to_csv
  tool: CONVERTER_ARRAY_TO_CSV
  input:
    - name: input
      value: "{{steps.fetch_data.result.stdout | fromJson}}"

- id: save_csv_file
  name: save_csv_file
  tool: OUTPUT_FILE
  input:
    - name: content
      value: "{{steps.convert_to_csv.result}}"
    - name: filename
      value: "user_data_export.csv"
    - name: fileType
      value: "csv"

Example: Sales Report Generation

- id: calculate_sales_metrics
  name: calculate_sales_metrics
  tool: PYTHON_SANDBOX_RUN
  input:
    - name: code
      value: |
        import json
        from datetime import datetime, timedelta
        
        # Sample sales data
        sales_data = [
            {"product": "Widget A", "sales": 1500, "region": "North", "month": "Jan"},
            {"product": "Widget B", "sales": 2300, "region": "South", "month": "Jan"},
            {"product": "Widget A", "sales": 1800, "region": "North", "month": "Feb"},
            {"product": "Widget B", "sales": 2100, "region": "South", "month": "Feb"}
        ]
        
        # Create summary table
        summary = [["Product", "Region", "Month", "Sales", "Growth"]]
        
        for i, item in enumerate(sales_data):
            growth = "N/A"
            if i >= 2:  # Calculate growth for Feb data
                prev_sales = sales_data[i-2]["sales"]
                growth = f"{((item['sales'] - prev_sales) / prev_sales * 100):.1f}%"
            
            summary.append([
                item["product"],
                item["region"], 
                item["month"],
                f"${item['sales']:,}",
                growth
            ])
        
        print(json.dumps(summary))

- id: export_sales_csv
  name: export_sales_csv
  tool: CONVERTER_ARRAY_TO_CSV
  input:
    - name: input
      value: "{{steps.calculate_sales_metrics.result.stdout | fromJson}}"

- id: create_downloadable_report
  name: create_downloadable_report
  tool: OUTPUT_FILE
  input:
    - name: content
      value: "{{steps.export_sales_csv.result}}"
    - name: filename
      value: "sales_report_{{date | format('YYYY-MM-DD')}}.csv"
    - name: fileType
      value: "csv"

Example: Image to PDF Conversion

- id: input_images
  name: input_images
  tool: INPUT_IMAGE_FILE
  input:
    - name: file_url
      value: "https://example.com/chart1.png"
    - name: description
      value: "Sales chart image"

- id: convert_chart_to_pdf
  name: convert_chart_to_pdf
  tool: CONVERTER_IMAGE_TO_PDF
  input:
    - name: file_link
      value:
        - "https://example.com/chart1.png"

- id: convert_multiple_charts
  name: convert_multiple_charts
  tool: CONVERTER_IMAGE_TO_PDF
  input:
    - name: file_link
      value:
        - "https://example.com/chart1.png"
        - "https://example.com/chart2.png"
        - "https://example.com/chart3.png"

Example: HTML to PDF / DOCX Conversion

- id: generate_html_report
  name: generate_html_report
  tool: OPENAI_INVOKE
  config:
    - name: version
      value: gpt-4
  input:
    - name: prompt
      value: |
        Generate an HTML report summarizing this month's sales results.
        Return only the HTML content.

- id: convert_report_to_pdf
  name: convert_report_to_pdf
  tool: CONVERTER_HTML_TO_PDF
  input:
    - name: html_content
      value: "{{steps.generate_html_report.result}}"
    - name: filename
      value: "monthly_sales_report"

- id: convert_report_to_docx
  name: convert_report_to_docx
  tool: CONVERTER_HTML_TO_DOCX
  input:
    - name: html_content
      value: "{{steps.generate_html_report.result}}"
    - name: filename
      value: "monthly_sales_report"

# steps.convert_report_to_pdf.result is a signed URL to the PDF file
# steps.convert_report_to_docx.result is a signed URL to the DOCX file

Example: Complex Data Transformation

- id: extract_complex_data
  name: extract_complex_data
  tool: EXCEL_GET_ROWS
  input:
    - name: file_url
      value: "{{steps.input_excel.result.file_url}}"
    - name: range
      value: "A1:F1000"

- id: transform_and_clean_data
  name: transform_and_clean_data
  tool: PYTHON_SANDBOX_RUN
  input:
    - name: code
      value: |
        import json
        import re
        
        # Parse the Excel data
        excel_data = {{steps.extract_complex_data.result.content}}
        
        # Clean and transform data
        cleaned_data = []
        headers = ["ID", "Name", "Email", "Phone", "Status", "Notes"]
        cleaned_data.append(headers)
        
        for row in excel_data[1:]:  # Skip header row
            if len(row) >= 6:
                # Clean email
                email = row[2].strip().lower() if row[2] else ""
                
                # Format phone number
                phone = re.sub(r'[^\d+]', '', row[3]) if row[3] else ""
                
                # Clean and escape notes (may contain commas, quotes)
                notes = str(row[5]).replace('"', '""') if row[5] else ""
                
                cleaned_row = [
                    str(row[0]) if row[0] else "",
                    str(row[1]).title() if row[1] else "",
                    email,
                    phone,
                    str(row[4]).upper() if row[4] else "PENDING",
                    notes
                ]
                cleaned_data.append(cleaned_row)
        
        print(json.dumps(cleaned_data))

- id: convert_cleaned_data
  name: convert_cleaned_data
  tool: CONVERTER_ARRAY_TO_CSV
  input:
    - name: input
      value: "{{steps.transform_and_clean_data.result.stdout | fromJson}}"

- id: save_cleaned_export
  name: save_cleaned_export
  tool: OUTPUT_FILE
  input:
    - name: content
      value: "{{steps.convert_cleaned_data.result}}"
    - name: filename
      value: "cleaned_data_export.csv"
    - name: fileType
      value: "csv"

Data Format Support

CSV Conversion

Input: 2D arrays, JSON arrays, structured data
Output: RFC 4180 compliant CSV
Features: Automatic escaping, encoding handling

Image to PDF

Supported formats: TIFF, PNG, JPEG, GIF, and more
Input: Up to 10 image URLs per conversion
Output: PDF document URL

HTML to PDF / DOCX

Input: HTML content string and optional filename (without extension)
Output: Signed URL to the generated PDF or DOCX file
Features: Useful for turning LLM-generated HTML into downloadable documents

Best Practices

CSV Handling

Always include headers in your data arrays
Handle special characters (commas, quotes, newlines)
Use consistent data types within columns
Validate data before conversion

Image Conversion

Ensure images are accessible via public URLs
Consider file size limitations
Optimize images before conversion for better performance
Use appropriate filenames for organization

Use Cases

Data Export: Export database queries to CSV for Excel
Report Generation: Convert processed data to spreadsheet format
Document Creation: Convert charts and images to PDF
Data Migration: Transform data between different systems
Analytics Export: Export analysis results for stakeholders
Backup and Archive: Convert data to portable formats
Integration: Prepare data for external system import

​Overview

​Key Features

​CONVERTER_ARRAY_TO_CSV

​CONVERTER_IMAGE_TO_PDF

​CONVERTER_HTML_TO_PDF

​CONVERTER_HTML_TO_DOCX

​Authentication

​Example: Data Export to CSV

​Example: Sales Report Generation

​Example: Image to PDF Conversion

​Example: HTML to PDF / DOCX Conversion

​Example: Complex Data Transformation

​Data Format Support

​CSV Conversion

​Image to PDF

​HTML to PDF / DOCX

​Best Practices

​CSV Handling

​Image Conversion

​Use Cases