What this category of data looks like
Data for this category comes from the national medical insurance information platform, hospital HIS and medical insurance interfaces, medical insurance catalog and coding library, and cross-regional medical settlement platform. The update frequency is daily. Daily processed documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. The data includes standardized diagnosis and treatment project, drug and consumable codes, expense amounts, business occurrence time, insured person identity identifiers and other fields, with no additional custom non-standard fields.
What constraints do these characteristics impose on the model access and configuration link
This category has multiple data sources with daily updates. Model access configuration must adapt to the call formats and rate limits of multi-source interfaces. The data includes standardized coding fields. The model must accurately match medical insurance catalog items. The context length must cover complete settlement documents and associated original policy texts. A single business involves multiple related documents. The total token count for a single request must be controlled to avoid timeouts. Data auditing involves medical insurance fund security. The randomness of model output must be reduced to avoid incorrect matching results.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the total token count of a single medical insurance settlement list, associated prescriptions, and policy summaries |
maxTokens | 2000–3000 token | Controls the length of audit conclusions output by the model in a single request to avoid request timeouts |
quoteMaxToken | 4000 token | Limits the total length of recalled original policy text fragments to ensure context does not exceed limits |
temperature | 0.1–0.3 | Reduces output randomness to improve the accuracy of policy matching and coding verification |
AIPROXY_API_ENDPOINT | Access path corresponding to internally deployed models | Adapts to the access requirements of institutional internal interfaces |
AIPROXY_API_TOKEN | Exclusive access key assigned by the institution | Ensures identity legitimacy for interface calls |
Three common configuration errors
- Symptom: A graphics card initialization failure error occurs when calling a locally deployed large model. Logs display driver abnormalities. Cause: The model running environment adapted to the target graphics card is not configured. Or the driver version is incompatible with the large model framework.
- Symptom: After switching to an image understanding model, the model returns empty fields. Cause: The model input format adaptation is not correctly configured. Or necessary decoding configuration is not included in the request parameters.
- Symptom: Domestic large models cannot be selected as call objects within the platform. Cause: The API endpoint and access key for the target model are not configured. Or the access list has not been updated with the interface configuration of the corresponding manufacturer.
How to confirm configuration is complete
- Initiate a test request for a single medical insurance settlement document. Verify whether the returned result includes accurate references to associated policies.
- Check the model call logs. Confirm that the request format matches the configured API endpoint and key, with no format errors.
- Adjust the
temperatureparameter. Observe the consistency of output results. Verify that randomness control meets audit requirements. - Batch import test data updated on the current day. Verify that the response time of interface calls meets the time requirements of business processing.
The values provided are common starting points and should be validated against the deploying institution'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.