What data for this category looks like
Data for this category comes from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. The update frequency is monthly. Routine documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Each document includes fixed fields: institution ID, inspection date, rectification items, and law enforcement officer ID. Inspection dates follow standard date formats. Rectification items are unstructured text descriptions. Penalty case files include penalty reason and result fields.
Constraints imposed by these characteristics on tool calling and plugins workflows
Multi-source data access requires tool calling to adapt to API formats and authentication methods of different systems. This prevents data pull failures caused by interface differences. Monthly update frequency requires tool calling to configure scheduled trigger rules. This ensures synchronization of the latest institution qualification, inspection records, and rectification data each month. The mixed structured and unstructured document structure requires plugins to support both structured field extraction and unstructured text parsing. This prevents missing key rectification items. The high-risk business attribute requires tool calling to add a result verification step. This prevents erroneous data from entering the ledger summary process.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to multiple types of tool calling needs, automatically selects appropriate plugins to perform ledger summary and report generation |
functionCall | Enabled | Supports structured field extraction to accurately obtain key data such as institution ID and inspection date |
| Scheduled trigger rule | Execute at 00:00 on the 1st of each month | Matches the monthly business data update rhythm, ensures ledger data is the latest monthly data |
| Multi-source API timeout setting | 30 seconds | Adapts to interface response delays of health systems, prevents data pull failures caused by timeouts |
| Tool retry count | 2 times | Addresses temporary exceptions in single interface calls, improves data pull success rate |
| Result verification threshold | Calibrated based on actual testing | Adjusts verification pass standards according to field integrity requirements for different types of documents |
Three common configuration mistakes
- A 500 error is returned when importing health-related documents. The cause is failure to adapt to the chunking rules for unstructured documents, which prevents the large language model from properly parsing law enforcement record text.
- Parameters are parsed normally but the HTTP tool call to pull supervision data does not execute. The cause is failure to configure permission verification parameters for the tool, and the target system blocks unauthorized requests.
- An error indicating only current usage is allowed is returned when calling a locally deployed BGE model. The cause is failure to correctly configure the model's access whitelist, and the FastGPT service IP was not added to the allowed list.
How to confirm configuration is complete
- Manually trigger tool calling to pull the practice license data of a single medical institution. Check whether fields such as institution ID and validity period can be correctly extracted.
- View tool calling logs to confirm that the scheduled task at 00:00 on the 1st of each month has been triggered, and there are no timeout or permission error messages.
- Call the locally deployed model, input a text fragment from a supervision inspection form, and confirm that structured rectification item extraction results can be correctly returned.
- Simulate an unauthorized request, check whether the tool blocks abnormal calls, and confirm that the permission configuration is effective.
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.