What the data for this category looks like
This category's data mainly comes from health supervision information systems, medical institution electronic registration systems, complaint reporting platforms, and law enforcement inspection records. The update frequency is monthly. Daily processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data fields cover institutional qualification information, on-site inspection records, rectification tracking status, penalty basis, and more. Only native business fields and original document text are retained. No additional custom statistical indicators are added.
What constraints these characteristics impose on the "workflow orchestration" link
Monthly updated data sources require workflows to trigger full or incremental data synchronization on a regular basis. Outdated institutional qualification and inspection records must be avoided. The mixed structure of multiple types of paper and electronic documents requires workflows to support multi-format document parsing and field extraction. It must adapt to fixed format differences across different documents. Strongly related law enforcement and rectification documents require workflows to associate institution IDs with corresponding case files. Cross-institution data confusion must be prevented. The document volume and field complexity processed in a single run require workflow configuration to limit the context length of a single call. Chaos in large model output must be prevented.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Bind institution ID and inspection date range | Data in this category is associated by institution and time dimensions. Precise filtering of corresponding monthly updated data source documents is required |
nodeId | Bind law enforcement case parsing node ID | This category requires associating law enforcement inspection records with corresponding rectification documents. Precise positioning of the corresponding workflow node is required |
| Single search limit | Top 5 entries | Core data for this category is law enforcement case files and rectification records for a single institution. Excessive recalled data will cause context overflow |
| Node timeout setting | 600 seconds | Law enforcement case files and rectification notices may contain multi-page scans or long text. Sufficient processing time must be reserved for parsing and field extraction |
| Maximum context length | 6000–8000 characters | Total length of a single set of associated documents for this category is moderate. This range can fully carry core policy basis, inspection records, and rectification requirements |
Three common mistakes
- Workflow returns empty chart after calling chart tool: The interface displays a blank area, and the interface returns an empty image URL. The cause is failure to bind institution qualification and inspection item parameters required for image generation in
variables. This prevents the large model from generating valid drawing instructions. - Workflow node returns 400 error: The node status shows failure, and the log returns a 400 status code. The cause is failure to correctly configure the binding fields of
variables. The passed institution ID format does not meet data source verification rules. - AI chat node returns empty or reports an error without processing: The workflow terminates directly, and the front end has no clear prompt. The cause is failure to configure a node exception trigger branch. No fallback processing logic is set for empty or error states.
How to confirm the configuration is correct
- Trigger a workflow run. Check if the institution ID and inspection date range bound in
variablescorrectly match the test data source documents. - View node logs. Confirm that the number of documents returned by a single search matches the preset top N configuration, with no redundant data.
- Simulate abnormal input. Verify that the fallback logic configured for node errors or empty states is executed correctly.
- Check parameter transfer during tool calls. Confirm that core fields required for chart generation have been correctly passed, with no missing values.
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.