What Data Looks Like for This Category
This category uses data from health supervision information systems, medical institution electronic registration systems, complaint and report platforms, and law enforcement inspection records. Updates occur on a monthly basis. Daily processing covers supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Documents include both structured fields such as institution name, inspection date, and practice scope, and free text content. No unified fixed units apply to this data, requiring adapted mixed-format information extraction and generation.
What Constraints These Characteristics Impose on Model Access and Configuration
Multiple data sources and fixed update cycles require model access configuration to support permission isolation and synchronization for multi-source data. Documents with both structured and free text require context parameters to cover long text processing needs, avoiding truncation of key compliance information such as institution qualifications and rectification requirements. The most time-consuming links, such as qualification verification and rectification opinion generation, require model output to maintain rigor and reduce random deviation. Compliance requirements for health system data demand strict distinction of call permissions for business scenarios, preventing data leaks or unauthorized access.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to long text content such as supervision documents and law enforcement records, avoiding truncation of key compliance information such as institution qualifications and rectification requirements |
temperature | 0.1–0.3 | Used for scenarios requiring rigor such as qualification verification and rectification opinion generation, reducing randomness of output content |
AIPROXY_API_TOKEN | Application-specific key bound to business scenarios | Complies with health system data permission control requirements, isolating call permissions for different supervision businesses |
AIPROXY_API_ENDPOINT | Locally deployed exclusive node address | Adapts to FastGPT v4.17.0 local deployment environment, meeting the compliance requirement of not allowing health system data to leave the domain |
vision | Enable as needed | Supports information extraction from image format content such as scanned medical institution practice licenses and on-site inspection photos |
maxTokens | 1500–2000 token | Adapts to the generation length of a single supervision opinion or penalty case file, avoiding incomplete output content |
Three Common Mistakes
- Phenomenon: API calls return a 401 permission error, or fail to retrieve institution qualification data from the specified knowledge base. Cause: Uses a universal API key, does not use an application-specific key bound to the business scenario, and does not adapt to the permission isolation requirements of the health system.
- Phenomenon: When creating a knowledge base in the local deployment version, the image understanding model cannot be selected. Cause: Did not pre-configure model dependencies related to
vision, and did not enable the corresponding function switch. - Phenomenon: Knowledge base indexing performance is poor, and cannot correctly extract fields such as institution qualifications and inspection items. Cause: Did not configure an indexing model adapted to mixed document formats, or did not select a model type adapted to health system documents.
How to Confirm Proper Configuration
- Call the FastGPT v4.17.0 test interface, pass in locally stored supervision inspection form samples, and check if returned results include core fields such as institution name, inspection date, and practice scope.
- View the API call log, confirm that the application-specific key bound to the business scenario is used, and no permission error messages appear.
- Upload a scanned medical institution practice license, and check if the model correctly extracts core information on the certificate.
- Trigger a knowledge base synchronization task, confirm that synchronization progress and update frequency meet the preset monthly cycle requirements.
- Check if the locally deployed external indexing model has correctly connected to the system, adapting to the mixed format processing requirements of health system documents.
The values listed above are common starting points, and should be evaluated 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.