What the data for this category looks like
Footwear smart due diligence report data primarily comes from brand supply chain ledgers, PDF-format quality inspection reports issued by third-party inspection institutions, product filing documents from cross-border e-commerce platforms, and sampling archive files from industry associations. Data update rhythm aligns with new SKU launches and quarterly quality inspection batches, with no fixed cycle.
Document structures include fields such as SKU number, upper/outsole material, production batch number, folding resistance count, anti-slip coefficient, and supplier qualification. Folding resistance count is measured in times, while anti-slip coefficient is a dimensionless value. Some documents include product photos and on-site quality inspection photos.
Constraints Imposed on Document Parsing and Chunking
The multi-source mixed format, professional inspection fields, embedded images, and strongly associated identifiers of footwear due diligence reports create multiple constraints for document parsing and chunking.
Multi-source documents cover different formats including Excel ledgers, PDF quality inspection reports with images, and customs declarations. Adaptation logic for different parsing engines must be supported. Professional inspection fields such as folding resistance count and anti-slip coefficient must be accurately bound to their corresponding inspection items. The association between parameters and values must not be split during chunking.
Embedded product photos and on-site quality inspection photos in the main text cause visual association information to be lost during plain text parsing. SKU numbers and production batch numbers are strongly associated identifiers. Chunking must retain all associated fragments under the same SKU to avoid cross-category information confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Footwear quality inspection reports often contain multiple pages of high-definition images, so OCR parsing takes a long time. The default timeout period is insufficient to complete full parsing. |
chunk_size | 800–1200 characters | Footwear documents include professional inspection parameters and SKU association information. Segment length must cover a single complete inspection item or SKU file to avoid splitting key associated information. |
chunk_overlap | 100–150 characters | Retaining segment overlap prevents key information such as SKU numbers and inspection items from being split across two segments, improving recall accuracy. |
ENABLE_IMAGE_OCR | Enabled | Footwear due diligence reports include embedded on-site quality inspection photos and product photos. Enabling OCR extracts text information from images and avoids ignoring image content. |
MAX_CHUNKS_PER_DOC | Calibrated via actual testing | A single footwear document may contain associated information for dozens of SKUs. Limiting the number of segments prevents invalid segments from occupying context quotas. |
filter_empty_chunk | Enabled | Batch-uploaded ledger documents may contain blank lines or incorrectly formatted segments. Filtering these improves subsequent recall efficiency. |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Phenomenon: After uploading a footwear PDF containing on-site quality inspection photos, the knowledge base recall results do not include text information from the images. Cause: The
ENABLE_IMAGE_OCRconfiguration item is not enabled, and only the plain text content of the PDF is extracted. - Phenomenon: After uploading an HTML-format footwear customs declaration document, the knowledge base does not parse any valid content. Cause: No parsing adaptation rules for HTML documents are configured, and the default parsing engine does not adapt to the tag structure of this format.
- Phenomenon: After batch-uploading multiple footwear SKU ledgers, the recall results include fragments where SKU numbers are separated from corresponding inspection items. Cause: The
chunk_overlapparameter value is too small, causing strongly associated identifiers and content to be split across different segments.
How to Verify Correct Configuration
- Upload a single footwear PDF containing on-site quality inspection photos, check the parsed segment content to confirm whether detection parameter text from the images is included.
- Upload a document containing SKU numbers and corresponding material descriptions, verify that segments retain the binding relationship between SKUs and associated content.
- Run a batch upload test, upload multiple documents of the same category, confirm that parsing results have no chaotic fragments across SKUs.
- View the system parsing logs to confirm that no abnormal errors occur in steps such as OCR parsing and segment processing.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.