> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chunkr.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Understanding the output
## Supported segment types for Excel files
The Excel parser maps identified elements to Chunkr's existing segment types, ensuring seamless integration with your current workflow.
The following segment types are supported:
| Segment Type | Description |
| ------------- | ------------------------------------------------- |
| Table | Rectangular data structures with headers and rows |
| SectionHeader | Headers that divide content into sections |
| Title | Main document or sheet titles |
| Text | Regular text content |
| Picture | Charts, graphs, images, logos |
| Footnote | References or additional information at bottom |
| PageHeader | Content at the top of each page |
| PageFooter | Content at the bottom of each page |
## Excel Segment Output Structure
When processing Excel files, Chunkr returns segments with both standard properties (common to all document types) and Excel-specific metadata. Understanding this structure is crucial for working with Excel parsing results effectively.
### Standard Segment Properties
Each Excel segment contains the core properties found in all Chunkr outputs:
```json theme={"system"}
{
"content": "Generated content in HTML/Markdown format",
"text": "OCR-extracted raw text",
"segment_type": "Table|Text|Title|SectionHeader|Picture|etc",
"bbox": {"left": 100, "top": 150, "width": 300, "height": 150},
"page_number": 1,
"page_width": 1024,
"page_height": 768,
"image": "https://presigned-url-to-cropped-segment-image"
}
```
### Excel-Specific Properties: The `ss_` Keys
**The key difference between Excel outputs and regular document outputs are the `ss_` (spreadsheet-specific) keys.** These properties provide essential Excel context that doesn't exist in PDF or other document formats:
| Property | Type | Description |
| ----------------- | ------ | --------------------------------------------- |
| `ss_sheet_name` | String | Name of the worksheet containing this segment |
| `ss_range` | String | Excel cell range (e.g., "A1:D10") |
| `ss_cells` | Array | Array of Cell objects with detailed cell data |
| `ss_header_range` | String | Range of the header cells (if applicable) |
| `ss_header_text` | String | Text content of the header (if applicable) |
| `ss_header_bbox` | Object | Bounding box of the header (if applicable) |
| `ss_header_ocr` | Array | OCR results for the header (if applicable) |
### Why Excel Needs Special Keys
The `ss_` fields exist to work with native Excel values and maintain the mapping between identified content and the original Excel structure. **These fields only exist for spreadsheet files.**
The `ss_` keys hold state for what was identified and map it back to Excel cells and ranges, enabling you to:
1. **Map to original Excel cells**: Access the exact cells (`ss_cells`) that were identified as part of each segment
2. **Preserve Excel ranges**: Know the exact cell range (`ss_range`) each segment corresponds to
3. **Track sheet context**: Identify which worksheet (`ss_sheet_name`) the content came from
4. **Maintain Excel structure**: Work with native Excel data like formulas, cell values, and styling
### Complete Excel Segment Example
Here's what a complete Excel Table segment looks like:
```json theme={"system"}
{
"content": "| Product | Q1 Revenue | Q2 Revenue |
|---|
| Widget A | $50,000 | $75,000 |
| Widget B | $30,000 | $45,000 |
",
"text": "Product Q1 Revenue Q2 Revenue Widget A $50,000 $75,000 Widget B $30,000 $45,000",
"html": "| Product | Q1 Revenue | Q2 Revenue |
|---|
| Widget A | $50,000 | $75,000 |
| Widget B | $30,000 | $45,000 |
",
"markdown": "| Product | Q1 Revenue | Q2 Revenue |\n|---------|------------|------------|\n| Widget A | $50,000 | $75,000 |\n| Widget B | $30,000 | $45,000 |",
"segment_type": "Table",
"bbox": {"left": 100, "top": 150, "width": 400, "height": 100},
"page_number": 1,
"page_width": 1024,
"page_height": 768,
"image": "https://api.chunkr.ai/presigned-url...",
"segment_id": "uuid-segment-id",
"confidence": 0.95,
// Excel-specific properties
"ss_sheet_name": "Q2 Sales Data",
"ss_range": "A3:C5",
"ss_cells": [
{
"cell_id": "uuid-cell-1",
"text": "Product",
"range": "A3",
"formula": null,
"value": "Product",
"hyperlink": null,
"style": {"is_bold": true}
},
{
"cell_id": "uuid-cell-2",
"text": "Widget A",
"range": "A4",
"formula": null,
"value": "Widget A",
"hyperlink": null,
"style": null
}
],
"ss_header_range": "A3:C3",
"ss_header_text": "Product Q1 Revenue Q2 Revenue",
"ss_header_bbox": {"left": 100, "top": 150, "width": 400, "height": 25}
}
```
### Excel Chart Example
Charts and graphs in Excel are identified as `Picture` segments with spreadsheet context:
```json theme={"system"}
{
"content": "Sales trend chart showing quarterly growth from Q1 to Q4",
"text": "",
"segment_type": "Picture",
"bbox": {"left": 450, "top": 100, "width": 350, "height": 300},
"page_number": 1,
"image": "https://api.chunkr.ai/chart-image...",
// Excel-specific properties
"ss_sheet_name": "Dashboard",
"ss_range": "E1:K20",
"ss_cells": []
}
```
### Accessing Excel Metadata in Code
```python Python theme={"system"}
for chunk in task.output.chunks:
for segment in chunk.segments:
# Standard properties
print(f"Content: {segment.content}")
print(f"Type: {segment.segment_type}")
# Excel-specific properties (only populated for Excel files)
if segment.ss_sheet_name:
print(f"Sheet: {segment.ss_sheet_name}")
print(f"Range: {segment.ss_range}")
# Access individual cells
if segment.ss_cells:
for cell in segment.ss_cells:
print(f"Cell {cell.range}: {cell.text}")
if cell.formula:
print(f"Formula: {cell.formula}")
# Access header information if available
if segment.ss_header_text:
print(f"Header: {segment.ss_header_text}")
```
This Excel-specific metadata makes Chunkr's Excel parser uniquely powerful for AI applications that need to understand and work with spreadsheet structure and context.