What data for this category looks like
Data for this category originates from the Chinese Center for Disease Control and Prevention Information System, hospital HIS systems, infectious disease reporting systems, and laboratory testing systems. Updates occur daily. Documents processed daily include infectious disease reporting cards, epidemic analysis reports, case correction records, clustered outbreak reports, and epidemiological investigation reports. Data fields strictly adhere to legal infectious disease reporting regulations. Core items include case classification, reporting time limits, correction flags, laboratory test results, and no redundant custom fields.
What constraints these characteristics impose on model integration and configuration
Multi-source data correlation for this category requires model context to cover the full lifecycle of each case. Daily updated batch data must work with scheduled trigger review workflows. Legally required report field accuracy means model output must not have deviations. Traceability for correction records requires complete contextual associations to be preserved. Model integration must first adapt to the contextual length of multi-source data, configure scheduled task trigger rules, ensure review result compliance, and avoid missing key information required by legal reporting rules.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers associated context of multi-source disease control documents, including complete information from case reporting cards, correction records, and epidemiological investigation reports |
maxTokens | 1800–2200 token | Meets output length requirements for single case file review, avoids truncation of legally required report fields or review conclusions |
temperature | 0.1–0.3 | Reduces random generation probability, ensures compliance and consistency of review results, and meets the strict requirements of disease control review |
AIPROXY_API_ENDPOINT | Fill in according to intranet deployment environment | Adapts to intranet access restrictions of disease control systems, prevents external networks from failing to connect to model services |
AIPROXY_API_TOKEN | Fill in according to the key provided by the model service provider | Completes identity verification for model services, ensures normal access permissions |
toolChoice | auto | Calls tools as needed to verify legal infectious disease reporting rules, improves review accuracy |
Three Common Configuration Mistakes
- Phenomenon: The model outputs thinking content with no main body, or displays the prompt "Model stream response is empty, please check if model stream output is normal". Cause: The
maxTokensconfiguration value is lower than the output length required for the current task, ormaxContextdoes not cover multi-source associated data, leading to insufficient context. - Phenomenon: An error "No language model configured" appears when entering the workspace editing page. Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTparameters are not filled correctly, or the proxy network cannot access the target model service. - Phenomenon: The AI model option in the workflow is empty. Cause: Access permissions for the corresponding model have not been enabled in system configuration, or model key binding configuration has not been completed.
How to Confirm Configuration is Complete
- Enter the FastGPT v4.17.0 model integration configuration page, verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENentries match the deployment environment. - Upload a test infectious disease reporting card and correction record, submit a review task, check that the model output includes complete review results with no truncation or empty responses.
- Enter the workflow editing page, confirm that the AI model option displays available deployed models and can be selected normally.
- Manually trigger a daily update data review task, check that task execution status and output results meet expectations.
The values provided 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.