What the data for this category looks like
Footwear investment research data mainly comes from official brand supply chain ledgers, category trend reports released by industry associations, sales monitoring data from cross-border e-commerce platforms, and customs import and export category statistical documents. The data update rhythm varies by scenario: supply chain ledgers update in real time with new product launches and inventory adjustments, industry trend reports are released quarterly, and e-commerce sales data syncs daily.
Document structure is divided into three categories: SKU parameter documents (including shoe size conversion tables, material composition, sole wear resistance index and other fields), competitor benchmarking documents (including pricing ranges, channel layout), and terminal sales performance analysis documents. There are clear specifications for fields and units: shoe sizes are marked with US, CN, and EU standards, material proportion uses percentage units, sole wear resistance uses ten thousand times as the unit, and single shoe weight uses grams as the unit.
What constraints do these characteristics impose on the "model access and configuration" link
The multi-standard fields and segmented category parameter requirements of footwear investment research data require that model access must support multi-field parsing and standardization conversion. Real-time updated supply chain data requires that the configured recall logic supports high-frequency incremental synchronization to avoid the model calling outdated inventory or new product information.
The multi-document classification structure requires configuring category-adapted document chunking rules during model access to ensure that SKU parameters and sales data are not incorrectly spliced. Additionally, footwear data has a large number of cross-standard unit conversion requirements, so standard mapping rules must be preset in the model configuration to avoid analysis deviations caused by unit mismatches.
Furthermore, the daily synchronization feature of e-commerce monitoring data requires that the model's context window configuration adapts to the batch processing capability of high-frequency data to prevent single-call timeouts.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Most single footwear SKU parameter documents range from 1000 to 3000 characters in length. Combined with the demand for multi-document recall, sufficient context space is reserved |
RECALL_TOP_N | Top 8–12 entries | Footwear investment research needs to reference SKU parameters, competitor data and sales information simultaneously. Too many recall entries will dilute valid information, while too few will fail to cover all analysis dimensions |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Footwear supply chain ledgers often contain batch SKU parameter tables, and single file parsing takes a long time. The timeout threshold needs to be extended to avoid parsing failures |
UPLOAD_FILE_MAX_SIZE | 500 MB | Industry trend reports often include high-definition charts and batch data, so large file upload support is required |
SIMILARITY_THRESHOLD | 0.75–0.85 | Footwear SKU parameters have a large number of similar fields, so the threshold needs to be increased to filter low-match irrelevant documents and ensure that recalled content accurately matches investment research needs |
PROXY_URL | Fill in the dedicated proxy address according to the deployment environment | To connect to a locally deployed large model or third-party API, correct proxy link configuration is required to ensure stable model calls |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- When testing the model, the prompt "This is an image model, why is the image option not supported" appears. The cause is that the input format switch exclusive to image models was not configured during model access, and only the call logic for text models is enabled by default.
- After a local deployment restart, model calls fail with a
504 Gateway Timeouterror. The cause is that the deployed large model service and FastGPT container were not configured to start automatically with the system. After restarting, the service link is interrupted, resulting in model call timeout. - After connecting a custom model, the number of recall results is abnormal, only returning 1-2 entries. The cause is that the similarity threshold was not adjusted to the range adapted to footwear data. An overly high threshold filters out most valid recall content.
How to confirm the configuration is complete
- Upload a footwear SKU parameter document, check if the parsed fields fully include exclusive fields such as shoe size standards and material proportion, and confirm that the parsing rules adapt to the category data structure.
- Initiate a model call containing a footwear parameter query, verify whether the returned content covers multiple dimensions of investment research information including SKUs, competitors and sales, and confirm that the recall logic meets the configuration expectations.
- View the model call logs, confirm that the proxy address is configured correctly, and there are no errors such as connection timeout or authentication failure, to verify that the model access link is normal.
- Adjust the recall count parameter, test the returned results under different values, and confirm that the number of contents meets the needs of investment research analysis.
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.