What the data for this category looks like
Knowledge base data for apparel and home textiles comes primarily from brand-owned product manuals, supply chain-provided fabric test reports, sorted marketing scripts from offline stores and live streams, and official detail page copy. Update rhythms align with new product launch cycles and promotional activities. Bulk updates occur concentrated during new product seasons, with daily synchronized updates for fabric adjustments and promotional validity changes. Document structures include structured tables (such as size charts, ingredient lists) and rich text content (such as scenario-based marketing scripts, product selling point descriptions). Core fields include style number, color code, fabric weight (unit: g/㎡), yarn count (unit: count), washing water temperature, and marketing selling point tags.
What constraints these characteristics impose on knowledge base retrieval and recall
The mixed structured and rich text document structure requires chunking tools to retain structured field integrity and contextual relevance, avoiding splitting size charts or ingredient lists which causes field loss. Fabric and size parameters with clear units require the retrieval link to match field units, preventing incorrect association of parameters with different units. Frequently updated marketing materials and SKU-level independent content require the recall link to add validity filtering and SKU-level deduplication, preventing expired promotional content or irrelevant cross-SKU content from being recalled. Batch materials for multiple SKUs require chunking to use SKUs as the smallest unit, avoiding cross-SKU content confusion that reduces retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Apparel and home textile product detail pages mostly include paragraphs and tables. This range retains complete information and scenario descriptions for a single SKU |
chunk_overlap | 150–200 characters | Avoid splitting continuous structured content such as fabric ingredients and size charts during chunking, preserving field relevance |
recall_top_k | Top 6–8 results | Apparel and home textile marketing content mostly combines scenarios and products. A small number of recalled results covers scenario and product matching for user queries |
similarity_threshold | 0.72–0.80 | Distinguish product parameters of similar style numbers, avoiding recall of low-relevance SKUs |
filter_expired_docs | Enable and set a 30-day validity window | Promotional marketing materials are updated frequently, requiring automatic filtering of expired content |
parse_structured_table | Enable | Size charts and ingredient lists for apparel and home textiles are structured data. Enabling this setting retains field relevance 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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The proportion of duplicate content in adjacent chunks after chunking is too high. Cause: The value of
chunk_overlapis not set reasonably, causing content at chunk boundaries to be overly repeatedly split. - Phenomenon: Non-JSON garbled code or missing
metadatafields are returned when calling the retrieval interface. Cause: Theparse_structured_tableconfiguration is not enabled, causing table documents to be parsed as plain text and unable to generate standard retrieval result structures. - Phenomenon: The AI response includes general knowledge content outside the knowledge base. Cause: The knowledge base exclusive recall switch is not enabled, or the
system_promptis not used to explicitly limit use to only content within the knowledge base.
How to confirm configuration is complete
- Upload one apparel and home textile SKU document containing a size chart and fabric parameters, view the parsed chunk list, and confirm each chunk contains complete SKU style number information and associated fabric parameters.
- Initiate a query containing specific fabric weight and size, check whether the
similarity_scoreof retrieval results falls within the configured 0.72–0.80 range. - Upload one promotional material document marked with expired validity, wait 1 day, then initiate a query using the corresponding promotional keyword, and confirm the document is not recalled.
- Call the official retrieval interface, check whether the returned JSON format includes standard fields such as
chunk_contentandmetadata, with no garbled code or missing fields.
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.