What the data for this category looks like
The data for feed industry financing daily reports comes from three sources: the national feed industry monitoring system, enterprise submission data from local animal husbandry and veterinary authorities, and public industry transaction matching platforms. It is updated daily and released on a T+1 basis. The documents are standardized structured tables with these fixed fields: enterprise name, feed category (pig feed, poultry feed, aquafeed, etc.), financing amount (unit: ten thousand yuan), financing method (bank credit, supply chain finance, private placement, etc.), financing date, and repayment period (unit: day). There are no unified unstructured additional notes. Only clear business fields and unit identifiers are included.
What constraints these characteristics impose on tool calling and plugins
Scattered data sources require connecting multiple types of interfaces to obtain complete data. Tool calling must configure multi-source data sources and complete data alignment. The daily update rhythm requires the tool to use fixed scheduled triggers. This avoids repeated data pulling or missing the latest data. Fields have clear units, so add a unit verification step during tool calling. This prevents abnormal results caused by mismatched values and units. The structured table format requires the plugin to support standardized table parsing. Plugins can directly map target fields without extra processing of unstructured text.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
CRON Expression | 0 1 * * * | The feed industry financing daily report is released T+1, so pull the latest data at 1 AM daily |
Multi-source Data Source Whitelist | Ministry of Agriculture and Rural Affairs monitoring interface, industry association submission interface, transaction matching platform interface | Limit trusted data sources to avoid accessing non-compliant data |
HTTP Request Timeout | 300 seconds | Multi-source interface pulling may have response delays, reserve sufficient time to complete data acquisition |
Field Unit Verification Switch | Enabled | The data includes unit fields such as ten thousand yuan and day, verify that values match units to prevent abnormal results |
Structured Parsing Mode | Table Column Mapping | The feed industry financing daily report uses a standardized table format, directly map target fields to improve extraction efficiency |
The parameter values provided on this page are common recommended starting points for configuration. The 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 mistakes
- The tool call returns a 429 status code, and task execution fails. This happens when request rate limiting is not configured. Multiple calls to the official monitoring interface in a short time trigger platform current limiting.
- The token consumption shown in the tool call log does not match the actual token consumption returned by the third-party interface. This happens when the full-link token statistics switch is not enabled. FastGPT only counts model input tokens, excluding context data returned by tool calls.
- The extracted financing amount field lacks unit information, or displays "50 yuan" instead of "50 ten thousand yuan". This happens when the field unit verification switch is not enabled. Mandatory verification of unit fields in the data is not performed.
How to confirm the configuration is complete
- Manually trigger a tool call, check that returned data fields match the configured table column mapping rules, and units match expected values.
- View the tool call log to confirm no HTTP request timeout or current limiting error is triggered, and the data source returns a 200 status code.
- Verify the coverage of multi-source data sources, confirm all interfaces in the configured multi-source data source whitelist are included in the call scope.
- Test abnormal data scenarios, confirm the field unit verification switch takes effect, and can identify and process content that does not comply with unit specifications.
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.