What data looks like for this category
Data for the apparel and home textile category comes primarily from brand product archives, fabric supplier test reports, industry compliance inspection documents, e-commerce platform product detail pages, and offline store sales ledgers. Update rhythm follows new product launch cycles. Bulk updates occur during spring/summer and fall/winter launch seasons. Daily updates only happen for single product adjustments, compliance changes, or inventory fluctuations. Each document typically includes fields such as article number, brand, fabric composition, weight, yarn count, washing parameters, production batch, quality inspection number, and supply channel. Units include professional textile and apparel measurement units like g/㎡, S, and ℃. Document structure primarily uses structured tables, with accompanying text and image explanations.
What constraints these characteristics impose on retrieval and recall
Data for the apparel and home textile category is scattered across multiple sources, has inconsistent update rhythms, and mixes structured and unstructured content. These factors create multiple constraints for the retrieval and recall process. Multi-source data has differences in field naming and units. Cross-source field alignment and unit normalization must be completed before recall to avoid matching deviations. The coexistence of bulk and sporadic update rhythms requires configuring incremental synchronization rules. Retrieval library updates must only trigger for changed content. Structured fields such as fabric weight and yarn count need to support exact matching. Unstructured text such as washing instructions and design details needs to support semantic recall. Corresponding retrieval strategies must be configured separately. Some documents associate multiple products in the same series. Cross-document associated recall must be supported to meet the associated analysis needs of due diligence reports.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 8-12 results | Apparel and home textile due diligence reports need to cover multi-dimensional information including fabric, washing, and compliance. Too many results will exceed the context window limit, while too few will fail to cover all associated data |
similarity threshold | 0.72-0.85 | Semantic similarity for textile and apparel fields such as weight and yarn count needs to balance accuracy and recall completeness, to avoid missing compliance-related documents |
chunk length | 800-1200 characters | Single apparel and home textile documents contain structured tables and text-image explanations. Too long chunks will destroy semantic coherence, while too short chunks will split associated field information |
incremental sync trigger condition | triggered by document update time + custom tags | Bulk updates during launch seasons and sporadic daily changes coexist, so trigger scenarios for full and incremental sync must be distinguished |
reranked return count | top 3-5 results | Due diligence reports need to focus on core compliance and fabric parameter information. Reranking filters redundant recall results to improve context quality |
structured field retrieval switch | enabled | The apparel and home textile category contains a large number of structured fields. Enabling this switch enables exact field matching and improves retrieval accuracy |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After configuring the reranking model, the
is_rerankedfield in retrieval results returnsfalse. Cause: The reranking function switch is not enabled in the knowledge base retrieval node, or the input text length of the reranking model exceeds the preset limit. - Phenomenon: When dynamically specifying a knowledge base, there are no selectable items in the "Reference Variable" dropdown menu. Cause: The target knowledge base associated variable is not configured as an enumeration type, or the permission to "allow calls in retrieval nodes" is not enabled in global variables.
- Phenomenon: Global variables cannot be referenced as knowledge base parameters in a workflow. Cause: The global variable is not bound to the variable pool of the current workflow, or the data source type of the variable does not match the parameter requirements of "knowledge base selection".
How to confirm correct configuration
- Upload a single apparel and home textile product document, perform a single retrieval, and check whether the returned results contain exact matching content for the target fields.
- View the logs of the retrieval node, confirm that the input parameters of the reranking model match the length of the segmented documents, and the
rerank_resultfield returns a normal status. - Configure dynamic variables to trigger retrieval, verify that the preset global variable options are displayed in the dropdown menu, and confirm that the variable scope covers the current workflow.
- Adjust the
similarity thresholdandrecall count, compare the number of returned results under different configurations, and confirm that the retrieval results meet the information coverage requirements of due diligence reports.
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.