What the data for this category looks like
Data for textile manufacturing intelligent due diligence reports comes primarily from upstream raw material spot and futures platforms, downstream brand order systems, customs import and export databases, and monthly statistical reports from industry associations. Update frequencies vary by data type. Raw material price data updates daily. Factory capacity data updates weekly. Industry-wide statistical reports release monthly. Documents include structured tables of capacity, unit price, and payment terms, plus unstructured equipment inspection reports and supply chain contract scans. Core fields include yarn count density, loom operating rate, and grey fabric weight. Corresponding units are count, unit, and grams per square meter.
What constraints do these characteristics impose on deployment and upgrade?
The data characteristics create multiple constraints for deployment and upgrade. Multiple data sources with varying update frequencies require differentiated scheduled sync tasks during deployment to avoid sync conflicts and data redundancy. Documents contain both structured tables and unstructured scanned files, so a mixed parsing mode must be configured to balance professional field recognition and image OCR performance. Textile manufacturing has highly recognizable specialized terminology, so a custom entity dictionary must be loaded in advance during deployment. Entity recognition errors will occur otherwise. The upgrade process must synchronously migrate custom dictionaries and multi-data source configurations to prevent configuration loss from version updates.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Textile manufacturing due diligence reports often include high-resolution factory equipment photos and bulk import/export documents. Single file size is large, so this setting must accommodate large file upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Batch parsing multiple industry reports and supply chain documents takes extended time for data association and format conversion, so this setting requires a longer timeout |
maxContext | 12000-15000 characters | Textile manufacturing supply chain data has many associated fields. A longer context enables logical cross-field association to improve the accuracy of due diligence report generation |
recall count | Top 8 entries | Prioritize accuracy of specialized data entries in segmented fields. Too many retrieved entries introduce irrelevant industry information and reduce report professionalism |
similarity threshold | 0.75-0.82 | Specialized terminology in textile manufacturing is highly recognizable. A threshold that is too low introduces irrelevant non-textile data, while a threshold that is too high misses valid associated information |
custom entity dictionary path | ./custom_dict/textile_manufacture.json | Pre-configure exclusive entities such as yarn count density and loom operating rate to improve recognition accuracy of professional fields |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: Enabling internet search fails to retrieve real-time quotation data for textile raw materials. Cause: No site whitelist for vertical textile industry data sources is configured, and the default search scope does not cover specialized segmented industry platforms.
- Issue: Running
docker build -f ./projects/on Windows 11 returnsexit code 137. Cause: Docker memory allocation is insufficient, and no sufficient resources are reserved for parsing large textile due diligence documents and bulk documents. - Issue: Custom entity dictionaries are lost after upgrading from version 4.8.23 to 4.9. Cause: The upgrade script does not synchronously migrate segmented configuration files in the
./custom_dictdirectory, resulting in failure of specialized term recognition.
How to Verify Correct Configuration
- Upload a local textile factory capacity report, verify that parsed fields include professional fields such as yarn count density and loom operating rate, to confirm the custom entity dictionary is active.
- Run the pre-configured multi-data source sync task, check system logs to confirm that update times for raw material price and order data match the preset frequency, to confirm multi-data source configurations are correct.
- Initiate an intelligent due diligence query, check the association accuracy of specialized terms and the number of retrieved entries in the returned results, to confirm the
recall countandsimilarity thresholdconfigurations are active. - Run the version upgrade script, check that the
./custom_dict/textile_manufacture.jsonfile remains intact, to confirm custom configurations are not lost.
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.