What the data for this use case looks like
Ledger data for elderly care service institution supervision typically originates from four sources: elderly care service information platforms, civil affairs statistics systems, fire inspection records, and institution operation ledgers. Updates occur monthly. Commonly used documents include filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. Core fields include institution name, number of beds, inspection date, and rectification status. Units of measurement include sheets, times, and days. Some unstructured documents require format conversion before they can be used for summary and analysis.
Constraints Imposed on Tool Calling and Plugins Workflows
Scattered data sources with a fixed monthly update cycle require tool calling to support cross-system data pulling and scheduled trigger configuration. Differences in field formats across various documents mean plugins need custom field mapping rules to support multiple ledger types including filing, subsidy, and inspection ledgers. Long verification times for supervision data require tool calling to automatically complete cross-data-source field comparisons to reduce manual workload. As errors may affect subsidy distribution and safety supervision links, plugins need built-in basic check logic to filter abnormal data.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapt to multi-source heterogeneous supervision data pulling requirements, automatically select appropriate plugin nodes |
functionCall | required | Force plugins to complete ledger field extraction and summary, avoid missing core supervision data |
| Scheduled trigger cycle | 00:00 on the 1st of each month | Match the monthly update frequency of supervision ledgers |
| Plugin input field mapping | Map by institution ID, number of beds, inspection date | Align with the core field standards of elderly care service supervision ledgers |
| Retry count | 3 times | Address network fluctuations or temporary interface exceptions during cross-system data pulling |
| Single pull data limit | 1000 entries | Adapt to the regular scale of monthly elderly care institution supervision data |
Three Common Misconfigurations
- Phenomenon: No input or output parameter bar appears after adding a custom plugin to the workflow, and no error messages are shown in logs. Cause: The parameter structure of
functionCallis not correctly declared in the plugin configuration, causing the workflow to fail to recognize plugin input and output items. - Phenomenon: Parsed result fields are empty after uploading elderly care service ledger files, making subsequent summary operations impossible. Cause: The output path of the document parsing tool is not correctly passed to subsequent plugin nodes, breaking the data link.
- Phenomenon: A timeout error occurs when calling the ledger summary interface, with the response time exceeding normal thresholds. Cause: The call scope of
toolChoiceis not restricted, leading to excessive parallel cross-system requests and exceeding the interface's bearing capacity.
How to Confirm Successful Configuration
- Manually trigger plugin calls, check if returned results include core supervision fields such as institution name, number of beds, and inspection date.
- View workflow operation logs, confirm that
functionCallis correctly triggered and no parameter verification errors occur. - Test the scheduled trigger task, confirm that ledger data from corresponding data sources is automatically pulled at 00:00 on the 1st of each month.
- Simulate a cross-system data pulling failure scenario, check if the plugin automatically triggers the preset number of retry operations.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.