What the Data for This Category Looks Like
This category of data originates from the Chinese Center for Disease Control and Prevention Information System, medical institution HIS systems, infectious disease reporting systems, and laboratory testing systems. Updates occur daily. Daily processed documents include infectious disease reporting cards, epidemic analysis reports, case correction records, clustered epidemic reports, and epidemiological investigation reports. Each document includes fields such as basic case information, diagnosis results, reporting time limits, correction instructions, and testing data. These fields follow a legally standardized format and must strictly align with the unified rules for infectious disease reporting.
Constraints Imposed on Model Access and Configuration
The standardized format of multi-source data requires model access to adapt to a unified parsing logic. The high-frequency daily update requirement means configurations must support scheduled synchronization and trigger-based invocation. Documents range from long-text epidemiological investigation reports to structured reporting cards, so model context and output token configurations must cover content of varying lengths. The strict requirements of statutory reporting demand stable, consistent model output to avoid deviations. Errors directly relate to epidemic early warning and regulatory requirements, so configurations must include basic validation logic to reduce abnormal output.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets the context loading requirements for long documents such as epidemiological investigation reports and epidemic analysis reports, ensuring full extraction of all key information within the reports |
maxTokens | 2000–3000 token | Meets the output length requirements for detailed explanations of service consultation responses, case verification results, and surveillance summary data |
temperature | 0.1–0.3 | Ensures output consistency for case diagnosis verification, time limit checking, and matching of statutory reporting rules, aligning with business strictness requirements |
AIPROXY_API_ENDPOINT | Fill in the corresponding address according to the internal deployment node | Adapts to the access restrictions of the disease control system’s internal network, ensuring network connectivity for model invocation |
AIPROXY_API_TOKEN | Bind a dedicated access key | Ensures the security of internal business data and model invocation, preventing unauthorized access |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfiguration Issues
- Symptom: Model invocation fails with the prompt "Model does not exist or cannot be accessed", and the log returns a specified error code. Cause:
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTare not correctly configured on the FastGPT platform, or internal access permissions for the model channel have not been enabled. - Symptom: Model output lacks case information fields, or fails to fully identify key content within epidemiological investigation reports. Cause: The value of
maxContextis too small to load the full document context, resulting in loss of key information. - Symptom: The model makes incorrect judgments when verifying case reporting time limits, or the output correction rule description contains deviations. Cause: The value of
temperatureis too high, leading to insufficient output strictness that deviates from the standardized requirements of statutory reporting.
How to Confirm Successful Configuration
- Use the model testing interface on the FastGPT platform to input sample text of a single standardized infectious disease reporting card, and verify whether the field extraction results from the model cover all required fields.
- Configure a scheduled synchronization task to verify whether daily updated data pulled from the disease control information system can normally enter the model invocation process, with no network or permission errors.
- Simulate a service consultation scenario, input standard questions about case corrections and clustered epidemic reports, and verify whether the model’s response conforms to business logic and statutory reporting rules.
- Check the model invocation logs to confirm there are no
maxTokenslimit exceeded errors, and that response times meet the usage requirements of the internal system.
The values provided here 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.