What Data for This Category Looks Like
Data sources for due diligence in the apparel and home textile sector include brand internal SKU management systems, third-party fabric testing institutions, e-commerce platform sales backends, and public industry association documents. Data falls into two categories: structured ledgers and unstructured files.
Structured data includes fields such as style number, fabric composition, gram weight, production batch, and inspection number. Units include g/㎡, pieces, batch numbers, and similar metrics.
Unstructured data mostly consists of multi-page PDF inspection reports and supply chain traceability documents.
Update frequencies vary by data type: SKU ledger data updates weekly, inspection reports sync with production batches, sales data updates daily, and industry capacity data updates quarterly.
What Constraints These Characteristics Impose on Tool Calling and Plugins
Structured data’s specific unit requirements mean tools must automatically match and verify field units to avoid parameter errors from unit mismatches.
Multi-page PDF inspection reports (unstructured data) require plugins with integrated OCR and structured parsing capabilities. These plugins must adapt to parsing duration and format for long documents.
Differing update rhythms across data sources require differentiated timing for tool calls. For example, call sales data in real time, and trigger inspection report parsing on demand.
Scattered data sources require tool calling to support independent authentication and batch configuration for multiple APIs. This covers all data sources needed for full due diligence workflows.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
apiCollection | A collection bound to fabric testing APIs, SKU ledger APIs, and e-commerce sales APIs | Covers all data sources required for apparel and home textile due diligence |
UPLOAD_FILE_MAX_SIZE | 200 MB | Adapts to the upload requirements of multi-page PDF inspection reports |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Adapts to the OCR and structured parsing duration of multi-page PDFs |
apiAuthType | Configure API Key authentication separately for each API | Adapts to the independent authentication rules of different data sources |
triggerCondition | Trigger based on data type: structured queries trigger ledger APIs, multimodal files trigger OCR parsing plugins | Matches the calling timing for different data types |
unitAutoMatch | Enabled | Automatically verifies and matches unit fields such as fabric gram weight and piece count to prevent parameter mismatch errors |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Errors
- Symptom: Calls to the
apiCollectioninterface return message: Invalid URL, code: 500. Cause: Third-party API domain whitelist verification is not completed, or the API interface path is configured incorrectly. This causes requests to fail proper routing. - Symptom: The system returns error
InternalError.Algo.InvalidParameter: Multimodal file size is [specific value]. Cause: TheUPLOAD_FILE_MAX_SIZEparameter is not configured, or the uploaded inspection report PDF exceeds the default limit. This leads to multimodal parsing failure. - Symptom: The tool calling module output cannot be canceled, and unexpected results are returned. Cause: Output filtering rules are not configured, or the tool’s original debug output is mistakenly set as a required return item. Result truncation configuration is not enabled.
How to Confirm Successful Configuration
- Initiate a single SKU ledger API call. Check if returned results include correct style numbers, fabric composition, gram weight and other fields, and that units match expected values.
- Upload single-page and multi-page PDF inspection reports. Check if parsed structured fields are complete, with no garbled characters or missing content.
- Simulate triggering different types of data calls. Confirm only the corresponding data source APIs are called, with no redundant requests.
- Configure output filtering rules. Check if tool calling results only return fields required for due diligence, with no original debug information.
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.