What the Data for This Category Looks Like
Footwear industry research report data originates from public industry association reports, brand supply chain ledgers, e-commerce platform sales monitoring data, and segmented category analysis from professional consulting institutions. Update cadence includes quarterly full industry report updates and monthly channel sales data updates. Document structures include modules such as category segmentation, material composition, cost breakdown, channel proportion, and competitor dynamics. Fields include SKU code, ex-factory unit price, retail unit price, and sales volume measured in pairs. Some reports include environmental compliance indicators and patent application information.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The multi-field attributes of footwear research reports (unit price, sales volume, material tags, etc.) require vector models to support mixed-field encoding. This avoids losing numerical information in single-text vectors. There are many segmented product categories and fine-grained tags. Indexing must support multi-level classification indexing to avoid recalling irrelevant category report content. Numerical fields such as sales volume and unit price must be normalized before being incorporated into vectors. Otherwise, these fields will be overwhelmed by text features. Quarterly full research reports and monthly incremental sales data coexist. Indexing must support task scheduling for parallel incremental updates and full refreshes. This prevents service interruptions during full updates.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Footwear research report single-segment text contains multiple fields such as materials and costs. Excessive length will destroy semantic association, while insufficient length will lose context |
rerank_model_enable | Enabled | There are many segmented product category tags. Basic vector recall easily mixes in irrelevant content. The reranking model can filter low-relevance results |
vector_db_index_type | HNSW | Footwear research report recall needs to balance speed and accuracy. HNSW index is suitable for fast retrieval of high-dimensional vectors |
numeric_field_normalization | Min-Max normalization | Resolves scale differences between numerical fields such as unit price and sales volume and text vectors |
recall_top_k | Top 15 entries | Reserves a sufficient recall pool for reranking model screening, adapting to the feature of many segmented product categories in footwear |
rerank_top_n | Top 5 entries | Controls the length of returned results to avoid information overload from research reports, meeting the needs of debugging scenarios |
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.
Three Common Errors
- Phenomenon: Online recall test results do not show the filtering effect of the reranking model. The number of returned entries is consistent with basic recall. Cause: The
rerank_model_enableswitch is not enabled in the index configuration of the target knowledge base, or global enablement is not associated with the current knowledge base. - Phenomenon: A
400 Bad Requesterror occurs during indexing model calls. Cause: Normalization parameters are not configured for numerical fields such as unit price and sales volume of footwear research reports, resulting in vector dimension mismatch. - Phenomenon: Custom multimodal vector models fail to generate vectors normally. Cause: The request format of the multimodal interface is not adapted in the vector model configuration, or the model call key and endpoint address are not filled correctly.
How to Confirm Proper Configuration
- Enter the index configuration page of the knowledge base, check whether the
rerank_model_enableswitch status and the values ofrecall_top_kandrerank_top_nparameters match the preset configuration. - Upload a sample of footwear research reports to trigger a manual index task, and check whether the task log contains successful records of numerical field normalization and vector encoding.
- Launch an online recall test, enter a query containing keywords of footwear segmented product categories, and compare the number of entries and relevance between basic recall and reranked results.
- Check the vector model call log to confirm that the request parameters and returned result format of the multimodal vector model (if enabled) meet expectations.
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.