What the Data for This Category Looks Like
Data for this category comes from medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable trace codes, and complaint clues. The update frequency is monthly. Daily processed documents include audit notices, inspection work papers, suspicious point lists, interview records, decision letters, and rectification reports. Typical document structures include main information, suspicious point details, compliance verification basis, handling suggestions, and attachment lists. Fields cover business-related content such as designated institution name, violation description, and associated settlement serial number.
Constraints Imposed on Model Access and Configuration
This category’s data sources cover multiple systems with fixed update cycles. The most time-consuming daily tasks include filtering suspicious points from settlement data. They also include retrieving medical records, prescriptions, and other materials. Model access configuration must adapt to context length limits for multi-source data. This prevents truncation of core supervision rules and suspicious point data. Fixed document structures and multi-field attributes for daily documents require configuration to support accurate extraction and verification of specific business fields. Compliance requirements for fund supervision mandate that model output maintains rigor. Configuration must adapt to monthly updated rule base data. This ensures the configuration can flexibly load the latest supervision rules.
How to Set the Configuration
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length after splicing multi-source medical insurance settlement data and rule base content, to avoid truncation of core supervision rules and suspicious point data |
maxTokens | 2000–3000 token | Meets the complete output requirements of audit documents, including the generation requirements of long documents such as decision letters and rectification reports |
temperature | 0.2–0.4 | Adapts to the rigor requirements of fund supervision, reduces output randomness, and ensures consistency between compliant expressions and business logic |
AIPROXY_API_TOKEN | Fill in the third-party model key applied for | Used for identity verification to ensure the legality of model calls |
AIPROXY_API_ENDPOINT | Fill in the interface address provided by the third-party model service provider | Points to the correct model call entry to ensure that requests can be properly delivered to the target model |
toolChoice | auto | Allows the model to independently choose whether to call tools, adapting to scenarios where rule base data needs to be retrieved during the audit process |
Three Common Misconfigurations
- A 422 status code is returned during calls, but local model call tests succeed. The cause is that the interface request parameter format does not match the target model’s requirements. It may also be that
AIPROXY_API_TOKENis not correctly bound to the corresponding model’s permission scope. - Only the third-party model key is filled when connecting an external model, and
AIPROXY_API_ENDPOINTis not configured. This causes requests to fail to point to the correct model service address. - Content truncation occurs when generating audit documents. The decision letter cannot be output completely. The cause is that the
maxTokenssetting value is smaller than the actual required length for generating the document. This results in forced truncation of the output.
How to Verify the Configuration Is Successful
- Fill in
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINT. Initiate a single call in the FastGPT model test interface. Check that the returned result has no format errors. - Upload simulated medical insurance settlement data and supervision rules. Test whether context loading works normally under the
maxContextconfiguration. Confirm there are no input truncation prompts. - Set
temperatureto 0.3. Generate 3 audit suspicious point lists. Check the consistency of output content expressions. Confirm that randomness meets expectations. - Configure
toolChoiceas auto. Trigger a test request that requires retrieving the rule base. Check whether the model can correctly call built-in tools to obtain supervision basis.
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.