What data for this category consists of
This category covers standardized documents related to civil affairs assistance and cross-department shared data. Daily documents include minimum living security application forms, family economic status verification reports, household survey forms, publicity materials, and similar items. Data sources are the low-income population dynamic monitoring platform, resident family economic status verification system, and cross-department shared data. The update frequency is once per month. Data fields include family income and property status, family member information, and content related to application eligibility. The structure aligns with standardized civil affairs business processes.
What constraints these characteristics impose on model access and configuration
Access to cross-department multi-source data requires adaptation to unified format standards, to avoid model recognition errors caused by differences in data fields. The monthly data synchronization requirement means configuration items must support regular incremental or full update rules. Standardized civil affairs documents and scanned materials require the model to have text and visual recognition capabilities, to ensure the accuracy of material review. Strict requirements for family privacy and assistance eligibility mean the randomness of model output must be limited, to ensure the compliance and consistency of judgment results.
How to configure the settings
The following configuration items apply to FastGPT v4.17.0:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of long-text family economic status verification reports and integrated content from multiple forms, covering the policy and material combined analysis scenario in party building learning Q&A |
temperature | 0.1–0.3 | Reduces random output, ensures the accuracy of minimum living security eligibility judgment and policy interpretation, avoids subjective deviation, and meets the rigor requirements of party building learning Q&A |
vision | Enabled | Used to recognize the content of scanned documents such as household survey forms and publicity materials, covering the material review and policy matching scenario in party building learning Q&A |
quoteMaxToken | 1500–2000 token | Limits the length of quoted document fragments, focuses on core verification fields such as family income and member relationships, and associates with policy requirements in party building learning |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Adapts to the access address of domestic large models, meets the data compliance requirements of civil affairs scenarios |
AIPROXY_API_TOKEN | Fill in according to the platform allocation | Ensures permission security for interface calls, complies with access control rules for government affairs data |
Three common configuration mistakes
- Phenomenon: The interface cannot select domestic large models such as Tongyi Qianwen, or returns an error that the model does not exist during calls. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the permissions of the corresponding model are not bound in the platform. - Phenomenon: After uploading a scanned household survey form, the model returns empty recognition results. Cause: The
visionconfiguration item is not enabled, or the required image transmission format is not used. - Phenomenon: The minimum living security eligibility judgment generated by the model has deviations, and the family income field is incorrectly matched. Cause: The
temperaturevalue is too high, or themaxContextsetting is insufficient, and complete cross-department shared data is not loaded.
How to confirm successful configuration
- Initiate a test call to verify the configuration of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and confirm that the interface returns normal model responses. - Upload a standard minimum living security application form scan to verify whether the
visionconfiguration takes effect, and whether the model can correctly extract core fields from the form. - Import a monthly updated family economic status data set to test whether the
maxContextandquoteMaxTokenconfigurations can fully load and process long-text content. - Adjust the
temperatureparameter to 0.2, initiate multiple calls for the same question, and confirm that the consistency of the output results meets expectations.
The values provided 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.