Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
Data for this category primarily comes from social insurance management systems, personal benefit record systems, labor relations and work injury identification systems, and the electronic social security card platform. The update frequency is daily. Commonly involved business documents include benefit application forms, payment duration certification documents, retirement approval forms, work injury identification materials, and unemployment benefit application materials. Data fields include personal social insurance enrollment information, payment duration records, benefit calculation basis, qualification review materials, and other items. There are no unified fixed measurement standards, and configurations must match the business rules of corresponding handling scenarios.
Constraints Imposed on the Model Access and Configuration Link
Multiple data sources require model access to support cross-system interface docking, preventing data fragmentation from harming the accuracy of consultation responses. The daily update frequency requires configuring scheduled synchronization tasks to ensure the model uses the latest benefit calculation and qualification review data during calls. Diverse business document structures require the model to adapt to parsing materials in different formats, accurately extracting core fields such as payment duration and work injury identification results. Non-standardized business fields require reserving field mapping rules during configuration to adapt to document format differences across different handling institutions. The risk of incorrect or missed benefit payments requires adding result verification logic during access to prevent the model from outputting incorrect benefit calculation conclusions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must carry the context of multiple social insurance management documents and payment records to avoid truncation of critical business information |
maxTokens | 2000–3000 token | Ensures complete response content for benefit calculation and qualification review, while controlling single-round response latency |
temperature | 0.1–0.3 | Benefit verification consultations require precise results; reduce randomness to avoid outputting unsupported benefit standards |
AIPROXY_API_ENDPOINT | Intranet-deployed proxy address | When the FastGPT server is located in an intranet environment, use an intranet proxy to connect to the large model API, avoiding public network access restrictions |
toolChoice | auto | Automatically select whether to call tools to query social insurance data based on user consultation content, improving response accuracy |
Three Common Configuration Errors
- Phenomenon: A connection timeout error occurs when calling the large model API, returning status code 504 or the message "unable to access target address". Cause: The FastGPT deployment server is located in an intranet environment, and no intranet proxy is configured to connect to the public network large model API, so a communication link cannot be established.
- Phenomenon: The benefit calculation result output by the model deviates from social insurance management rules. Cause: No mapping rules are configured for core fields such as payment duration and work injury identification results, and non-standardized document content is directly extracted, leading to incorrect information parsing.
- Phenomenon: The model response does not include the thinking process, only displaying the final conclusion. Cause: The corresponding configuration item is not enabled, or the selected large model does not support the thinking chain return format, so intermediate reasoning steps cannot be output.
How to Confirm Successful Configuration
- Test large model API calls in the intranet environment, confirm there are no connection timeouts or domain name resolution errors.
- Upload a combined document of a benefit application form and payment duration certification, test whether the model can accurately extract core fields and generate responses that conform to business logic.
- Adjust the
temperatureparameter to around 0.2, test the same consultation question multiple times, confirm there are no obvious random deviations in the response results. - View the model response log, confirm that the loaded context information is complete, and no critical business content is truncated.
The values provided are common starting points and should be evaluated against samples from the target deployment environment.
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.