What the data for this category looks like
Footwear investment research data primarily comes from official brand supply chain documents, industry association quality inspection reports, e-commerce platform sales ledgers, and shoe design specification files. Data update rhythms are divided into fixed cycles and ad-hoc updates: supply chain data is updated quarterly during fixed cycles, and e-commerce sales data is updated monthly. Ad-hoc updates include scenarios such as the release of new national standards and the launch of new products by core brands. Document structures include two categories: structured tables and unstructured text. Structured fields include shoe ID, shoe length (millimeters), single shoe weight (grams), wear resistance rating, price range, and others. Unstructured content includes design descriptions, quality inspection rules, marketing copy, and others.
What constraints these characteristics impose on the "context and token" link
The multi-field association characteristic of footwear investment research data requires that context recall retain complete shoe business units, avoiding token occupation by invalid fragments. The attribute differences of multiple SKUs lead to a large number of potential fragments for single-round recall. If no upper limit is set, the model's token quota will be quickly exceeded. The mixed document structure of unstructured text and structured tables increases segmentation difficulty. Improper segmentation will split core associated fields such as shoe ID and material, leading to broken context coherence. High-frequency update data sources require context recall to adapt to real-time synchronization requirements. Relying on fixed historical splicing cannot cover the latest changes in shoe parameters.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContextToken | 8000–12000 characters | Footwear investment research documents contain multi-field combinations, and single-segment recall content is relatively long, so sufficient tokens must be reserved to accommodate valid fragments |
recallTopK | Top 6–10 entries | Footwear has a large number of SKUs, so context related to associated shoe styles must be recalled accurately to avoid redundant token consumption |
chunkSize | 800–1200 characters | Footwear documents contain associated fields such as materials, quality inspection data, and sales data, so segmentation must retain complete business units |
chunkOverlap | 100–150 characters | Prevent associated fields such as shoe ID and material attributes from being split after segmentation, and maintain context coherence |
similarityThreshold | 0.75–0.85 | Filter low-relevance shoe documents to reduce invalid token occupation |
apiPassContext | Enabled | Support passing custom context via API to adapt to real-time investment research data synchronization scenarios |
The parameter values provided on this page are all conventional recommendations used to determine a starting point 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
- The context recall count displayed on the page is 30, but the number of fragments actually sent to the model exceeds expectations. The cause is that the
recallTopKparameter is not restricted, and the recall results for multiple footwear SKUs are not capped, leading to token overrun. - Segmented context cannot be associated with the corresponding shoe style. The cause is that the
chunkOverlapparameter is set too low, and associated fields such as shoe ID and material are split into different segments, losing context association. - The knowledge base token calculation result does not match actual consumption. The cause is that
tokenCalculatorModeis not used to calculate based on actual content character count, and fixed-length segmentation is mistakenly used as the calculation basis, leading to token estimation deviation.
How to confirm the configuration is correct
- View the knowledge base segmentation list to confirm that each segment contains complete shoe-related fields, with no obviously split attribute information.
- Initiate a test call, compare the recall count displayed on the interface with the number of fragments in the API request log, and confirm that the parameters are effective.
- After adjusting the
maxContextTokenparameter, verify that the model output does not show a token overrun error. - Pass custom context via API call, confirm that the model correctly uses the passed content and does not use system-spliced historical records.
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.