What the data for this category looks like
For financial institution-focused apparel and home textile intelligent due diligence reports, data sources include brand SKU ledgers, fabric and accessory procurement contracts, offline store sales weekly reports, e-commerce platform product detail pages, and third-party quality inspection reports. Data is updated primarily weekly or monthly. Most documents are multi-sheet Excel files, multi-page PDFs, or structured JSON. Core fields include fabric composition percentage, washing instructions, tag retail price, SKU code, and inventory turnover days. Units include gram weight (g/㎡), sizing (S/M/L), inspection grades, and more. Some documents include physical inspection images or scanned packaging labels.
What constraints these characteristics impose on the model integration and configuration step
Multi-source and heterogeneous document formats require parsing nodes to adapt to multiple import templates, to avoid field matching discrepancies. Large individual document sizes and dense fields lengthen parsing and inference durations, placing higher requirements on timeout thresholds. When batch processing multiple SKU due diligence data, a large number of parallel requests are generated, requiring a balance between concurrency levels and platform rate limiting rules. Some documents contain unstructured image content, requiring additional configuration of multimodal parsing parameters to ensure that test results in quality inspection reports can be correctly extracted. Ledger formats vary significantly across different brands, requiring adjustment of recall thresholds to accurately match core fields and avoid interference from irrelevant information.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 120-180 seconds | Apparel and home textile quality inspection reports and multi-SKU ledgers usually exceed the default 60-second parsing duration. This interval covers the parsing needs of most long documents |
batchMaxConcurrency | 3-5 concurrent requests | Single SKU due diligence data volume is large. Excessively high concurrency will trigger platform rate limiting, while excessively low concurrency will extend batch processing cycles. This interval balances efficiency and stability |
chunkSize | 800-1200 characters | Apparel and home textile data has dense fields. Too long segments will cause context confusion, while too short segments will lose associated information such as fabric composition and SKU codes |
similarityThreshold | 0.75-0.85 | Precise matching of core business fields is required to avoid recall of irrelevant document fragments. This threshold filters low-correlation parsing results |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some brands' quarterly supply chain summary documents have large sizes. This configuration adapts to large file upload and parsing requirements |
requestTimeout | 600 seconds | When calling large models across service providers, complex due diligence logic has long inference durations. This threshold avoids premature interruption of the complete inference process |
The parameter values provided on this page are all common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: A
408 Request Timeouterror is returned when calling a custom PDF parsing service, and the parsing task is interrupted. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default 60-second duration is insufficient to cover the parsing process of long documents. - Phenomenon: Batch execution nodes can complete the full process during online debugging, but only partial tasks are executed when called via the API. Cause: For batch nodes on version v4.9.3, the
batchTaskTimeoutparameter was not configured. The global timeout threshold of the API request is lower than the actual execution duration of the batch task, causing the request to disconnect prematurely. - Phenomenon: The requestid for large model requests cannot be obtained, and no corresponding identifier appears in platform logs. Cause: The log output configuration of the node was not enabled, or relevant log tracing parameters were not activated, so the unique identifier for a single request cannot be generated and recorded.
How to confirm that configurations are properly set
- Upload a single apparel and home textile quality inspection report over 100 MB, and check whether the parsing progress completes within the set timeout threshold without timeout errors.
- Call the batch execution interface to submit due diligence tasks for 10 SKUs, and confirm that all tasks complete within the API request's timeout threshold without task loss or abnormal interruptions.
- Trigger a large model call, check the platform logs, and confirm that the corresponding requestid field exists. This identifier can be used to trace the complete request chain.
- Test ledger documents in different formats (Excel, PDF), and confirm that core fields such as fabric composition and SKU codes extracted after parsing are not missing.
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.