Data Characteristics of This Category
Data related to apparel and home textile investment research comes primarily from brand product manuals, supply chain fabric test reports, industry association trend documents, e-commerce platform product details, and end-user reviews. Update frequency aligns with new product launch cycles. Updates are more frequent during spring/summer and autumn/winter new product seasons. Monthly updates are standard for daily operations. Document structures include structured specification tables, test reports with embedded tables, long-form research reports, and multimodal assets such as fabric swatch cards and finished product photos. Fields include exclusive information such as fabric component percentages, weight (unit: g/㎡), washing instructions, item numbers, pricing ranges, and supply chain delivery times.
Constraints Imposed by These Characteristics During Model Access and Configuration
Standardized fields in structured specification tables and test reports require connected models to support structured data extraction and unit consistency conversion. The presence of multimodal assets requires connected vector models to support multimodal input. Frequently updated data requires a shorter vector refresh cycle to avoid data lag. Long-form research reports and multi-page documents require configuring reasonable segmentation and context length parameters during model access, to prevent core information from being truncated. Differences in parsing across document formats also require the configuration process to support compatibility with multiple file types.
How to Set Configuration Values
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
multimodal_embedding_enabled | true | The apparel and home textile industry includes a large number of multimodal assets such as fabric swatch cards and finished product photos, so the multimodal vector extraction function must be enabled |
embedding_batch_size | 32–64 | Industry documents often contain structured tables and long text paragraphs. This batch size balances parsing speed and memory usage |
max_context_tokens | 8000–12000 | Industry research reports and supply chain documents are mostly long-form text. This range fully preserves core information such as fabric components and delivery times |
recall_top_k | Top 8–12 entries | Apparel and home textile investment research requires multi-dimensional recall covering fabric, supply chain, and market data. Excessive entries increase inference load |
similarity_threshold | 0.72–0.85 | Similarity judgment for industry data fields such as weight and component percentage requires a balance between precision and recall coverage |
parse_timeout | 120 seconds | Large fabric test reports and multi-page research reports take longer to parse. This setting prevents premature timeout |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: A
404 body not founderror is returned when configuring a multimodal vector model. Cause: No dedicated proxy address is configured separately for the multimodal model, which conflicts with the proxy settings of the text model. - Symptom: Fabric component percentage fields are parsed as empty when mixing index models and text models from different vendors. Cause: Field extraction rules across models are not unified, and structured fields for apparel and home textiles are not standardized.
- Symptom: Vector generation tasks time out after uploading multi-page supply chain documents. Cause:
embedding_batch_sizeis not adjusted based on the average length of documents. An excessively large batch size consumes too much memory, leading to timeout.
How to Verify Successful Configuration
- Upload a single fabric test report PDF, and check whether the vector generation log includes embedding records for multimodal data.
- Initiate a simulated investment research query, and verify that the number of returned documents matches the configured
recall_top_kvalue. - Upload multiple apparel and home textile documents in different formats, and check that parsed fields include exclusive information such as weight and fabric component percentage.
- View the proxy configuration panel, and confirm that proxy addresses and API keys for multimodal models and text models are configured separately.
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.