Page Context
Client industry: Footwear Business direction: Intelligent due diligence reports Capability area: Multi-turn dialogue and prompt engineering
What the Data for This Category Looks Like
Data sources for footwear intelligent due diligence include brand official supply chain archives, third-party quality inspection institution reports, cross-border e-commerce sales ledgers, and offline store stocking records. Update frequencies follow these rules: supply chain data is updated per production batch, quality inspection reports are updated per random inspection batch, and sales data is updated per calendar week. The structure of a single due diligence document includes basic shoe information, material details, production traceability information, compliance test results, and channel circulation records. Covered fields are shoe style item number, upper material, sole material, production factory code, formaldehyde detection value (unit: mg/kg), and net weight per single shoe (unit: g). Some regional documents use EU, US, and CN shoe size units.
Constraints for Multi-turn Dialogue and Prompt Engineering
Footwear data has multi-dimensional scattered characteristics. Multi-turn dialogue must gradually guide clarification of key identifiers. For example, confirm the shoe style item number before proceeding with subsequent queries, to avoid confusion between due diligence data for different styles of the same brand. Differences in shoe size units across regions require prompt engineering to define clear unified unit conversion rules, to prevent unit confusion in output results. The binding relationship between production batches and quality inspection reports requires dialogue to guide the provision of traceability information in order. Do not request too many parameters at once, to prevent users from missing key content. Material details have subdivided fields. Prompt engineering must confirm in rounds whether all material items (such as upper, lining, outsole) need to be covered, or if queries should only target specific parts.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Footwear due diligence reports include multi-dimensional traceability data. Retain key multi-turn dialogue information such as shoe style item numbers and quality inspection batches to avoid context loss |
recallTopK | Top 6–8 results | Footwear data fields are scattered. Recall sufficient associated documents to cover multiple dimensions including materials, traceability, and compliance |
similarityThreshold | 0.72–0.78 | Avoid recalling redundant data for non-target shoe styles, while covering minor differences across batches of the same item number |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Single footwear quality inspection reports may contain multiple test pages, leading to longer parsing times |
chunkSize | 1000–1500 characters | Information in fields such as footwear materials and traceability is concentrated. Overly long segments lose context, while overly short segments damage field integrity |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports batch uploading of large-volume documents including multiple supply chain ledgers and customs declarations |
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 targeted analysis, and testing on own samples is recommended before finalizing settings.
Three Common Misconfiguration Scenarios
- Symptom: Returns "Cannot read properties of null (reading 'q')" when called. Cause: Multi-turn dialogue context variables are not properly bound, resulting in failure of normal transmission of query parameters in the dialogue chain.
- Symptom: Cannot upload different types of footwear documents in a single round of dialogue, leading to data confusion. Cause: Multi-file classified upload rules are not configured in the workflow, and dedicated parsing prompts are not bound to different file types.
- Symptom: Low accuracy of the workflow question classification node. Cause: Prompt engineering classification tags are not adjusted for the footwear due diligence scenario, and recognition is not limited to three core question types: shoe style item number, material compliance, and production traceability.
How to Verify Correct Configuration
- Initiate a test dialogue, enter the target shoe style item number and production batch, and conduct multiple follow-up queries about materials and compliance test results. Confirm that the conversation interface fully retains earlier key information to verify context configuration effectiveness.
- Upload two different types of footwear documents: one quality inspection report and one supply chain ledger. Confirm that the workflow automatically assigns different files to corresponding processing nodes to verify multi-file upload rules are configured correctly.
- Trigger the question classification node, input "Does the formaldehyde content of this shoe meet standards?" and "Check the production factory for XX item number". Confirm that classification results accurately match preset compliance testing and production traceability tags to verify prompt engineering configuration is effective.
- Upload a single customs declaration over 300 MB. Confirm that the parsing task completes within 120 seconds to verify the timeout configuration is reasonable.
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.