What the Data for This Category Looks Like
Data related to footwear marketing comes primarily from brand product management systems, e-commerce platform listing backends, and offline store inventory synchronization APIs. Update frequency fluctuates with marketing cycles. It is higher during new product launch periods, and relatively stable during routine maintenance cycles. Each data entry includes fixed fields: product ID, style number, official name, upper material, outsole material, size chart, recommended selling price, core selling point copy, external link for real-shot images, and detail page link. Size fields support multiple unit formats such as EU, CN, and US. Selling prices use RMB yuan as the unit, and inventory uses pieces as the unit.
Constraints on Workflow Orchestration
Multiple data sources require workflows to configure multiple nodes for pulling product data from different systems. This prevents process failure caused by interruption of a single data source. Fluctuating update frequency requires flexible adjustment of the workflow’s scheduled trigger interval. This adapts to high-frequency update requirements during new product seasons. The multi-unit nature of fields requires adding a unit unified conversion step to the workflow. This ensures consistent size and pricing formats in marketing content. Long detail page text and rich attribute fields require text processing nodes in the workflow to adapt to segment length and context length limits. This avoids truncated generated content or missing attributes.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
dynamic_knowledge_base | Enabled, bound to the global variable selected_kb | Adapts to the dynamic switching needs of multiple knowledge bases categorized by style number for footwear (men's shoes, women's shoes, children's shoes, etc.) |
text_extract_field_map | Configure mapping style number→product_style, selling price→product_price, size chart→product_size | Footwear product data fields have a high degree of standardization, requiring mapping of original fields to variable names used uniformly by the workflow |
code_run_timeout | 300 seconds | Sufficient execution time must be reserved for multi-source data pulling and batch processing of footwear data, to avoid mid-run timeout interruptions |
ai_input_variables | Bound to extracted_product_data and marketing_prompt | Standardized product data extracted via code execution must be passed to the AI model to generate customized marketing content |
recall_similarity_threshold | 0.75–0.85 | Footwear marketing content requires matching precise product attributes. An overly high threshold will miss relevant materials, while an overly low threshold will introduce irrelevant content |
chunk_max_length | 800–1200 characters | Footwear detail page text is lengthy. Excessively long segments will cause context overflow, while excessively short segments will disrupt selling point coherence |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The
extracted_product_datafield output by the code run node is empty. Passing this data to the AI model triggers thechat:ai_input_is_emptyerror. Cause: No field mapping rule for footwear product data is configured, so raw data pulled from multiple sources is not correctly parsed and extracted. - Symptom: The global variable
selected_kbcannot dynamically switch knowledge bases. Marketing content matches product materials that do not correspond to the current style number. Cause:selected_kbis not bound to the classification rules for footwear style numbers, and only a single knowledge base is fixedly bound. - Symptom: The size data returned by the text extraction node has mixed formats, including multiple units such as EU, CN, and US. Cause: No unit unified conversion rule is configured, and raw size field data is directly extracted without standardization processing.
How to Verify Proper Configuration
- Trigger a complete workflow run, view the output logs of the code run node, and confirm that the
extracted_product_datafield includes expected fields such as style number, selling price, and size chart. - Manually modify the classification tag bound to the global variable
selected_kb, and verify whether the recalled materials from the knowledge base match the corresponding category of footwear products. - Review the marketing content generated by the AI model, confirm that the included product attributes match the extracted fields, and there are no format-confused unit information.
- Adjust the value of
recall_similarity_threshold, observe changes in the number of recalled materials, and confirm that the threshold is within a reasonable range.
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.