What the Data for This Category Looks Like
Footwear marketing content data primarily comes from product manuals of partnering footwear brands, e-commerce platform detail pages, official size charts, quarterly marketing script libraries and promotional event documents customized for financial institutions. Update schedules adjust based on new product launches, seasonal shifts, and marketing milestones. Update frequency is higher during new product launch periods. Regular product styles have an update cycle of 1 to 3 months. Most individual documents use a structured format, including fields such as product number, category, size range, material, selling price, and marketing selling points. Units mostly follow US/EU/CN size standards, centimeters, yuan, and similar units.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Footwear data includes numerous structured fields and multi-unit size information. This requires the retrieval process to support field-level precise matching and unit normalization processing. This prevents parameter deviations in marketing materials that financial institutions push to merchants, caused by inconsistent size units. High-frequency updates during new product launch periods require the vector database refresh cycle to support short-cycle incremental updates. This prevents recalled content from lagging behind the latest marketing activities, which would negatively impact merchant customer acquisition results. Marketing scripts and product parameters are stored in the same document. This requires setting differentiated recall weights for different content types. Ensure marketing selling points are prioritized for merchant marketing-related queries, while product parameters match technical inquiries. Product number is a core identification field. It must be a matching item with high retrieval priority, to improve precise recall rates. This helps merchants quickly obtain marketing materials for specific product numbers.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | Top 6-8 entries | Footwear marketing content documents have moderate information volume. Too many recalled entries will cause redundant context, while too few will fail to cover complete marketing selling points |
Similarity Threshold | 0.72-0.78 | Adapts to matching needs for precise fields such as footwear product numbers and sizes, balancing precision and recall coverage |
Chunk Length | 800-1000 characters | Footwear documents include long paragraphs such as size charts and material descriptions. This chunk length preserves complete associated field information |
Vector Database Incremental Refresh Cycle | 24 hours | Regular styles have an update cycle of 1-3 months. A 24-hour refresh covers high-frequency incremental updates during new product launch periods |
Field Matching Weight | Product number:1.5, Size:1.2, Marketing Selling Point:1.0 | Core identifiers for footwear are product number and size. Increasing the weight of these fields improves precise recall rates |
Unit Normalization Toggle | Enabled | Resolves inconsistent US/EU/CN size unit issues, avoiding retrieval deviations caused by unit differences |
The parameter values provided on this page are general recommendations that serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Extra spaces appear between numbers and text in knowledge base retrieval results. The size parameters displayed on the interface, such as "42 size", differ from the input "42size". Reason: The space cleaning configuration in the text cleaning link is not enabled, so redundant spaces from the original input are retained during parsing.
- Phenomenon: An unsupported format error is triggered when uploading an Excel-format footwear product table, and parsing cannot be completed. Reason: The Excel file parsing adaptation toggle is not enabled, and only general document format parsing configurations are activated.
- Phenomenon: After the question classification node in the workflow, the AI dialogue returns content but cannot exit the classification branch, and the classification logic is triggered repeatedly in a loop. Reason: No branch termination configuration is added after the AI dialogue node, causing the workflow to always return to the question classification node.
How to Confirm the Configuration Is Correct
- Upload a footwear product document containing multiple sizes and multiple units, and verify that parsed fields automatically complete unit normalization and have no extra spaces.
- Initiate a precise query containing product numbers and sizes, and check whether the number and similarity of recalled results fall within the preset configuration range.
- Upload an Excel-format footwear marketing material table, and verify that it can be parsed normally and imported into the vector database.
- Trigger a workflow test, execute AI dialogue after the question classification node, and confirm that the workflow can normally exit the classification branch and enter subsequent links.
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.