What the data for this category looks like
This data category comes primarily from internal wealth management business systems of joint-stock banks and public financial market data released by the National Interbank Funding Center. Full data updates are completed daily right after the close of trading. Data is stored as structured tables, with fields including product code, full product name, expected annualized yield range, 7-day annualized yield, ten-thousand share yield, establishment date, and statistical date. Yield-related fields use percentage units. Income amount fields use yuan units. Each single document contains full coverage of available and existing wealth management products for the day.
Constraints imposed on model access and configuration
The fixed field structure of structured data requires clear field mapping rules during access. This prevents extraction failures caused by mismatched field names. The fixed daily update rhythm requires configuring scheduled task scheduling parameters. This ensures the broadcast generation process starts immediately after data updates are completed. The complex structure with multiple fields requires configuring field filtering and sorting parameters. This prioritizes retaining core fields required for broadcasting. The unified unit requirement requires configuring unit alignment rules. This ensures the yield data output by the model matches the units of the original data. The large single-document data volume requires configuring context truncation parameters. This avoids exceeding the maximum input length supported by the model.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Matches the average length of a single daily report document, to avoid content truncation that would lose critical yield data |
scheduleCron | 0 18 * * * | Matches the regular data update time immediately after the close of daily trading for joint-stock banks, to ensure access to the latest full dataset |
fieldMapping | Map full product name to product name, map 7-day annualized yield to broadcast core field | Aligns original data fields with input fields required for model calls, to avoid missing fields |
fieldFilter | Retain expected annualized yield range, 7-day annualized yield, ten-thousand share yield | Filters out fields not required for broadcasting, to reduce model input redundancy |
unitAlign | Uniformly convert to percentage and yuan | Ensures the yield data output by the model matches the units of the original data, to avoid confusion |
responseMaxTokens | 1500 characters | Matches the typical output length of a single daily report broadcast, to ensure full coverage of all core product information |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- A
504 Gateway Timeouterror appears in the interface, and the backend log recordsTarget service response timed out. Cause: No fixed model service access address is configured, and address changes after container restart cause interruptions to the access link. - The broadcast content generated by the model includes internal reasoning text wrapped in
<think>tags. Cause: The model's native reasoning log output switch is not turned off, causing internal thinking content to be returned along with the result. - Calling an external search tool returns
No matching results, and the backend log showsInvalid request parameters. Cause: Search tool data source filtering rules are not configured correctly, and no reasonable timeout threshold is set, leading to invalid searches.
How to confirm the configuration is complete
- Navigate to the FastGPT model debugging interface, input test data for joint-stock bank yield daily reports, and check whether field mappings match correctly.
- Manually trigger a scheduled task, and verify whether the latest data is automatically pulled and broadcast content is generated after the set scheduled time.
- Review the model output results, confirm that no additional reasoning logs or error tags are included, and that the units of all fields match the original data.
- Check the backend logs, confirm that there are no error records such as
connection failedormissing fields, and that the data pull time matches the set scheduling rules.
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.