What data for this category looks like
Coal chemical industry yield-related data is primarily sourced from public monitoring data released by domestic coal industry associations and transaction records from bulk commodity spot trading markets. Data is published within one hour after market close each trading day. The data uses structured JSON format, including fields such as product code, product name, daily settlement price, benchmark settlement price, price change value, and pricing unit. The pricing unit is uniformly yuan/ton. Each entry corresponds to daily market information for a single coal chemical product, with a fixed number of fields per entry.
What constraints do these characteristics impose on model access and configuration
The structured data characteristics of the coal chemical category impose clear constraints on model access and configuration. First, adapt to structured JSON data sources, so enable a dedicated structured parsing mode to avoid field recognition errors caused by general text parsing. Second, data updates follow trading day cycles, so schedule task triggers to match trading day rhythm to avoid fetching outdated historical data. Third, data fields include numerical values with units, ensure the model correctly identifies pricing units to prevent parsing errors where values and units are separated. Fourth, the batch data structure for multiple products requires the model to support batch field extraction and organization.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
modelProvider | custom | Coal chemical market data requires calling dedicated data source interfaces, so select the custom model provider type |
baseURI | https://api.coal-industry.org/marketing/daily-report | Corresponding dedicated market data interface address for the coal chemical category, ensure accurate data for the target category |
apiKey | Exclusive access key bound to the interface | Restrict access only to the coal chemical dataset, avoid permission risks from general-purpose keys |
maxContext | 800–1200 characters | Adapt to the total field length of single coal chemical product data, prevent field loss from context truncation |
parseMode | structured | For structured JSON data, enable structured parsing mode to improve field extraction accuracy |
scheduleCron | 0 18 * * 1-5 | Match post-close update times for domestic trading days, trigger a data fetch task once per weekday |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration errors
- Phenomenon: The custom coal chemical data source does not appear in the model provider list. Cause: Failure to set
modelProvidertocustom, or theapiKeyformat does not meet interface verification rules. A 403 permission error may be returned in some scenarios. - Phenomenon: Test calls return empty result fields. Cause: Failure to configure
parseModetostructured. General parsing mode cannot recognize structured JSON data, resulting in failure to extract valid fields. - Phenomenon: No broadcast content is generated after the scheduled task triggers. Cause:
maxContextis set too small, causing price change values for some products to be truncated, making content splicing impossible.
How to confirm configuration is complete
- Enter the FastGPT model management interface, check the custom model list, confirm that the configuration entry for the coal chemical data source exists.
- Initiate a single manual call, check whether the returned result includes complete product code, name, settlement price and price change value fields.
- View the scheduled task running logs, confirm that the task for the most recent trading day has been successfully executed with no timeout or error records.
- Compare the original JSON data returned by the data source interface with the model-parsed result, confirm that the field mapping relationship matches the actual data.
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.