Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from social insurance handling systems, personal benefit records, labor relations and work injury identification systems, and the electronic social security card platform. It is updated daily. Daily processed documents include benefit application forms, payment period certificates, retirement approval forms, work injury identification materials, and unemployment insurance benefit application materials. Core fields cover benefit type, payment period, qualification verification result, application information, and more. The data structure is strictly bound to social insurance business processes, and only contains fields necessary for business operations.
What constraints do these characteristics impose on the model access and configuration link
This category of data is updated daily and bound to standardized business processes. Model access configuration must adapt to frequently refreshed data sources to avoid result deviations caused by expired context. Core documents contain fixed business fields. The model must strictly match the meaning of the fields when generating content, and should not conduct generalized interpretation. At the same time, the data is directly linked to benefit payments. The accuracy of model output directly affects business compliance. Configuration must prioritize ensuring the rigor of results.
How to set the configuration
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 12000–15000 token | Adapt to daily updated social insurance business data, ensure that the context covers complete information related to the benefit calculation cycle |
maxTokens | 2000–2500 token | Limit the length of single-round output, avoid generating redundant content beyond the scope of business form filling |
quoteMaxToken | 800–1000 token | Restrict the total length of referenced documents, ensure that referenced content focuses on core business fields and qualification verification information |
toolChoice | auto | Allow the model to independently determine whether to call tools to handle complex calculation logic such as payment period verification |
temperature | 0.1–0.3 | Reduce output randomness, ensure the rigor and accuracy of content related to benefit verification |
vision | false | No image-based content is included in the business documents of this category, so visual recognition function does not need to be enabled |
AIPROXY_API_ENDPOINT | Fill in according to the actual deployed proxy address | Adapt to the network access restrictions of internal social insurance systems, ensure connectivity for model calls |
AIPROXY_API_TOKEN | Fill in according to the actually assigned interface key | Complete permission verification for model calls, avoid interface access being blocked |
Three common mistakes
- Phenomenon: The model returns the error
{"object":"error","message":"Only allowed now"}when called. Cause:AIPROXY_API_TOKENor proxy address is not configured correctly, leading to failed interface access permission verification. - Phenomenon: Locally deployed model service cannot be connected, with the prompt connection refused. Cause: The correct model service address is not filled in the FastGPT configuration, or the local model service has not opened the corresponding network port.
- Phenomenon: The generated party building learning Q&A content deviates from the business details of social insurance benefit verification. Cause: The
temperatureparameter is set too high, leading to generalized output lacking business specificity.
How to confirm successful configuration
- Initiate a test query for payment period verification, check whether the model output references the correct business document fields
- View the model call log, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no connection errors - Adjust the
temperatureparameter and compare the output results, confirm that the output rigor meets business requirements - Test the synchronization of frequently updated data sources, confirm that the context refresh configuration adapts to the daily update rhythm
The values provided here are common starting points, and should be evaluated against samples relevant to the specific deployment.
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.