Data Characteristics for This Category
Apparel and home textile investment research data sources include brand-side public supply chain ledgers, fabric composition test reports, offline terminal sales ledgers, online e-commerce platform sales reports, industry research reports, and exhibition materials. Data is updated on multiple schedules: supply chain ledgers are updated weekly, terminal sales data is updated daily, and industry research reports are released quarterly. Document structures include multi-chapter text, structured tables with fields such as SKU codes, fabric weight, and cost proportion, charts, and CSV-formatted sales details. Field units include grams, pieces, ten thousand yuan, square meters, and other category-specific identifiers.
Constraints for Vector Models and Indexing
Mixed multi-type documents require vector models to support semantic encoding of both unstructured text and structured tables. This avoids splitting core information such as fabric composition and SKU associations. Frequently updated sales and supply chain data requires near-real-time indexing mechanisms. This prevents investment research bias caused by delayed data. Category-specific fields and units require the indexing process to retain precise entity semantics. This avoids confusion with fields from other textile and apparel categories. Long-form research reports require segmented processing that balances semantic completeness and indexing efficiency. This prevents context overflow from overly long segments, or entity breakage from overly short segments.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Apparel and home textile research reports contain long-form industry analysis and structured tables. A segment length within this range preserves semantic connections and avoids breaking the association between SKUs and fabrics |
chunk_overlap | 100–150 characters | SKU codes in supply chain ledgers and sales reports often span segments. Overlapping segments preserve entity associations and prevent loss of critical information during indexing |
vector_store_type | Local vector database with hybrid indexing support, available in version 4.9.0 and above | Apparel and home textile data includes unstructured research reports and structured SKU data, requiring vector storage and retrieval that supports multiple data types |
top_k | Top 20 results | Investment research scenarios require coverage of multi-dimensional supply chain, sales, and research information. This value balances recall coverage and context load |
similarity_threshold | 0.72–0.80 | Entities such as fabric compositions and SKU codes in apparel and home textiles have high semantic distinctiveness. This threshold filters irrelevant cross-category search results |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing multi-page supply chain ledgers and exhibition materials for apparel and home textiles takes significant time. The default threshold does not cover the full parsing process |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Vector recall results include a large volume of irrelevant data from non-apparel and home textile categories, and the number of returned results exceeds the preset range. Cause:
similarity_thresholdis not configured, or the threshold is set too low, and entity filtering is not applied to category-specific fields such as SKU codes and fabric compositions. - Symptom: Indexing tasks return 504 timeout error status codes. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and parsing time for multi-page supply chain ledgers in apparel and home textiles exceeds the default threshold. - Symptom: GraphRAG association results lack the association between SKUs and fabric types. Cause: Only flat text vector recall is enabled, entity indexing functionality is not activated, and core entities in apparel and home textile data are not extracted.
How to Verify Correct Configuration
- Upload an apparel and home textile supply chain ledger document, and check if parsed segments in the knowledge base retain the association between SKU codes and corresponding fabrics, and if segment lengths match the preset configuration.
- Submit a query such as "the fabric composition of a certain home textile product", and verify that the number of recall results matches the
top_kconfiguration, and that similarity scores fall within the preset threshold range. - After enabling the GraphRAG function, confirm that association results include the association relationships of category-specific entities such as SKUs and fabric types.
- Check the vector database monitoring panel, and confirm that the execution success rate of indexing tasks remains in a stable range, with no frequent timeout 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.