Benefits of Using MD vs XLSX for Knowledge Base on Dify

Why Use Markdown?

1. Better AI Processing

• Semantic understanding: AI models process continuous text more effectively than fragmented cell data
• Context preservation: Paragraph-based content maintains relationships between information
• Effective retrieval: Vector embeddings capture meaning better from natural language text
• Natural chunking: Content splits logically by sections, preserving context in each chunk

2. Cost Efficiency

• Smaller storage: Plain text (5-10KB) vs Excel with formatting overhead (50-100KB+)
• Lower token usage: Markdown structure is simpler, reducing embedding and processing tokens
• Faster processing: Text parsing is significantly faster than Excel binary format

3. Operational Benefits

• Version control friendly: Git tracks line-by-line changes effectively
• Universal editing: Any text editor works, no proprietary software needed
• Better collaboration: Merge conflicts are easier to resolve in plain text
• Automation ready: Easily integrated into CI/CD and documentation workflows

4. When to Use Excel?

XLSX may be suitable when:

• You need structured tabular data with calculations/formulas
• Data is primarily numerical with specific formatting requirements
• Direct import/export with database systems or business intelligence tools
• Non-technical users need to edit data in familiar spreadsheet interface

However, for knowledge bases consumed by AI, converting to Markdown yields better results even for tabular data.

Demo: Converting XLSX to MD

You can create a custom plugin tool on Dify to convert Excel files to Markdown. Here’s how I built mine:

Implementation Steps

1. Accept XLSX file input
   • Require Xlsx File parameter and wrap its blob in a BytesIO stream
2. Configure column selection
   • Extract Selected Columns parameter (accepts list/JSON string/comma-separated string)
   • Ensure it is non-empty
3. Set delimiter
   • Resolve Delimiter parameter for separating entries
4. Parse Excel file
   • Read the first worksheet into a DataFrame using pandas
   • Verify all requested columns exist in the DataFrame header
   • Subset DataFrame to selected columns only
   • Normalize NaN values to None
5. Transform to structured data
   • Convert each row into a dictionary keyed by selected column names
   • If no rows remain, emit message indicating no data and stop
6. Generate Markdown
   • Build content by writing column: value lines per row
   • Append delimiter between entries
   • Join all blocks into final Markdown
7. Output file
   • Derive filename from uploaded file metadata
   • Emit blob message with Markdown bytes and metadata

Sample

Input

 

Output