What the Data for This Category Looks Like
Footwear market and yield rate data is primarily sourced from vertical footwear trading platforms, brand inventory and sales systems, and sales data from mainstream e-commerce platforms. Full data for the previous calendar day is updated daily at midnight. Each daily report document uses a structured format, including fields such as brand name, footwear SKU, category tag, unit purchase cost (yuan), unit terminal selling price (yuan), daily sales volume (units), regional sales distribution (unit count statistics by province), and no percentage-based proportional fields. Each document corresponds to full sales and cost data for all footwear styles for a single day.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
The multi-source, dispersed nature of footwear data requires multi-turn dialogue to first clarify the data source scope required by the user, and prompts must preset filtering rules. The daily full update feature requires dialogue context to be bound to a specified date to avoid mixing data across days. The high number of detailed SKUs and lack of proportional percentage fields require prompts to clearly define field extraction priorities and unit requirements. During multi-turn dialogue, users must be guided to refine query dimensions such as brand and category to prevent overly broad returned results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000 characters | A single footwear daily report document is approximately 8000-10000 characters; retaining sufficient context avoids truncating critical SKU and field information |
systemPromptTemplate | "Only return corresponding fields based on the uploaded footwear market daily report data, according to the brand and category dimensions specified by the user. Do not use percentage expressions. Confirm the data date range during multi-turn dialogue" | Matches the non-percentage requirement of footwear data, and clarifies context constraints and query dimensions for multi-turn dialogue |
retrievalTopK | Top 15 entries | Footwear has a large number of SKUs; sufficient relevant entries must be recalled to cover users' detailed query needs |
similarityThreshold | 0.72–0.78 | Footwear category tags have high granularity; a threshold that is too low will introduce irrelevant SKUs, while a threshold that is too high will miss accurately matched entries |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single footwear daily report file requires processing a large number of SKU data; this avoids task interruption due to parsing timeout |
promptRetryCount | 2 retries | Footwear data has many fields; user prompts may contain ambiguity; retries can correct parameter extraction deviations |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: Calling the dialogue interface returns a
422 Unprocessable Entityerror, with a prompt indicating missing required fields. Cause: The required fields of footwear data (such as SKU, terminal selling price) are not explicitly required in the system prompt, causing the AI-generated response format to not comply with interface verification rules. - Issue: The returned footwear data in the dialogue contains percentage expressions. Cause: Percentage format is not disabled in the system prompt, and the default percentage output logic for general financial scenarios is used, which does not meet the field requirements of footwear data.
- Issue: The uploaded footwear daily report file is incorrectly split into multiple small fragments, resulting in incomplete recall results. Cause: General title separators (such as
#,##) are used, and the SKU row structure of footwear data is not adapted, causing the segmentation logic to deviate from the actual organization form of the data.
How to Verify Proper Configuration
- Upload a single footwear market daily report file, initiate a basic query, and verify that the returned results do not contain percentage expressions and include the specified field information.
- Initiate a multi-turn dialogue, sequentially inquire about footwear data for different brands and categories, and verify that the system can correctly trace back context and switch query dimensions.
- Adjust the
similarityThresholdvalue to verify that the SKU coverage range of the recall results meets expected requirements. - Simulate a large file upload scenario, and verify that the file parsing is not interrupted within
300 secondsand can normally return complete results.
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.