What the data for this category looks like
Intelligent due diligence data for textile manufacturing mainly comes from customs declarations for upstream raw material purchases, factory production ledgers, ERP documents from downstream customer order systems, supply chain logistics waybills, and test reports issued by third-party fabric quality inspection institutions. The update frequency varies significantly: raw material purchase data is updated monthly, production ledgers are updated daily, and customer order data is synchronized in real time with transactions. Most documents are in PDF or Excel format with multi-page nested tables. Core fields include yarn count (unit: count), loom speed (unit: revolutions per minute), grey fabric width (unit: centimeters), raw material inventory (unit: tons), order delivery lead time (unit: days), and other related fields.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-field and specific unit attributes of textile manufacturing due diligence data require multi-turn dialogue to retain the unit and time dimensions of the context, to avoid field matching errors caused by context truncation. The long document structure requires the dialogue system to limit the context length of a single round of input, to prevent the model from confusing production data from different batches. Data with different update frequencies must have their time ranges clearly marked in the prompt, otherwise the model may mix monthly purchase data with real-time production data. In addition, the nested table document structure requires the prompt to specify parsing rules to ensure that extracted fields are accurately bound to their corresponding units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the multi-page nested table content of textile manufacturing due diligence reports, retains sufficient context to avoid separation of units and fields |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports single combined files containing multiple declarations and ledgers, adapts to batch upload of due diligence materials |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large nested tables take longer to parse, prevents parsing failure from mid-run interruptions |
chunkSize | 1000–1500 characters | Adapts to long field paragraphs in textile manufacturing data, avoids losing the association between units and fields after splitting |
similarityThreshold | 0.75 | Distinguishes similar fields with different units under the same category (such as yarn count and fabric density), reduces matching errors |
recallTopK | Top 6 entries | Balances recall accuracy and context length, prevents excessive irrelevant textile manufacturing data from occupying dialogue window space |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfiguration Scenarios
- Symptom: When uploading textile manufacturing ledger files in a mirrored deployment environment, the interface shows the parsing status as "pending" with no progress updates, and ultimately no analysis results or error logs are generated. Cause: The
PARSE_FILE_ENABLEDparameter was not set totrueduring deployment, or there is a network connectivity issue between the file parsing container and the main container. - Symptom: After calling a file link variable in a dialogue, the specified textile manufacturing raw material inventory data cannot be associated, and the returned analysis content is unrelated to the target file. Cause: The field range bound to the variable was not specified in the prompt, causing the model to match irrelevant production order data.
- Symptom: In the 4.6.9 version of the advanced orchestration workflow, calling the AI dialogue node after a judge block fails to retrieve the initial user due diligence query, and the AI reply does not include the core request of the initial query. Cause: The
original_user_querysystem variable was not bound to the output port of the judge block, causing the initial user question to not be passed to subsequent nodes.
How to Verify Correct Configuration
- Upload a textile manufacturing ERP ledger file containing nested tables, check that the parsing completes within 300 seconds, and that the extracted fields include accurate unit information.
- Input a query that includes "yarn count for Q1 2024", check that the dialogue context retains the time dimension and unit information, with no field confusion.
- Add a judge block to the 4.6.9 version of the advanced orchestration workflow, configure the output port to bind the
original_user_queryvariable, and verify that subsequent AI dialogue nodes can retrieve the initial user question. - Upload textile manufacturing due diligence files in both PDF and Excel formats, check that all formats are parsed correctly with no format errors.
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.