What Data for This Category Looks Like
Data sources include professional Chinese herbal medicine circulation monitoring platforms, national Chinese herbal medicine price index systems, and compliant public industry quotation data. The update frequency is once daily, with that day's market data collected and released by 17:00 local time. The document structure uses a single structured record per product, including fields such as product name, specification grade, producing area, same-day average transaction price, previous day's average transaction price, and cycle average price. The unit for average prices is yuan per kilogram, with no additional statistical fields attached.
Constraints on Model Access and Configuration Imposed by These Characteristics
The multi-product, structured data characteristics of the Chinese herbal medicine category impose three constraints on model access and configuration. First, single records have fixed fields but cover a large number of products. Configure batch data sharding parameters to avoid exceeding the model's input limit for a single call. Second, the fixed daily update schedule requires scheduled task triggers to align model calls with the data collection rhythm. Third, field names vary across multiple data sources. Configure data mapping rules to unify field formats from different data sources and prevent model parsing errors. Additionally, clear unit requirements mean unit validation logic must be embedded in the model prompt to ensure output complies with business specifications.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The character volume of a single batch of Chinese herbal medicine market data typically falls within this range, matching the input window limits of most general-purpose large models |
response_format | forced JSON_OBJECT | Business requirements mandate structured yield and market daily reports to facilitate subsequent automated distribution and display |
schedule_cron | 0 17 * * * | Chinese herbal medicine market data is collected and released by 17:00 daily, so the scheduled task trigger time aligns with the data update rhythm |
batch_size | 20–30 items per call | Balances single-batch processing efficiency and model load, avoiding overloading the model with a single call's data volume |
request_timeout | 600 seconds | Parsing and generating multi-product data requires extended processing time, preventing task interruption due to timeout |
field_mapping | Calibrated based on actual testing | Field names differ across data sources. Adjust field mapping rules based on the actual connected data sources to ensure the model correctly identifies input data |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: After model access, the configuration option for structured response formats is not displayed in the interface. Cause: The format adaptation switch was not enabled in the advanced settings of the model access page, or a model type that supports structured output was not selected.
- Issue: A 405 status code is returned after sending a request. Cause: The configured model interface request method does not match the platform's requirements, or interface access permissions have not been fully authorized.
- Issue: Model generation takes longer than expected. Cause: The number of Chinese herbal medicine products processed in batch exceeds the configured batch_size limit, or the maxContext parameter was not adjusted to match the data volume.
How to Confirm Successful Configuration
- Trigger a single test task, verify that the returned structured data includes all fields mapped in the configuration, and that field units match the business agreement.
- Check the scheduled task execution logs to confirm that task trigger times align with the data update rhythm, with no abnormal interruption records.
- Submit batch test data, verify that the daily report generated by the model covers all input products, with no redundant or missing information.
- Check the model call response logs to confirm that no abnormal status codes or timeout records appear in requests, and that they comply with the configured parameter requirements.
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.