What this category of data looks like
Daily documents for this category include low-income security application forms, family economic status verification reports, household survey forms, democratic appraisal records, public notice materials, and assistance approval forms. Data comes from the low-income population dynamic monitoring platform, resident family economic status verification system, and shared data from public security, human resources and social security, medical insurance, housing construction, and tax authorities. The update frequency is monthly. Core fields cover family income, property status, member relationships, and basis for assistance eligibility determination. Most content is structured or semi-structured text, with no fixed-proportion image attachments.
What constraints these characteristics impose on model access and configuration
Cross-departmental multi-source data access requires configuration items to adapt to the call formats of different systems. The monthly update frequency requires the model’s processing scope to cover the latest information from monthly dynamic monitoring. Strict verification requirements for core fields mean the model output must accurately match preset fields, avoiding vague inferences. For scenarios without fixed image attachments, do not enable image recognition capabilities. Also avoid losing key verification information from cross-departmental data due to context truncation, to ensure the compliance of audit results.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | This scenario needs to accommodate multiple cross-departmental documents and complete case content, to avoid truncation of key verification data |
maxTokens | 4000–6000 tokens | The text length of a single core review material falls within this range, to ensure complete parsing of single documents |
temperature | 0.1–0.3 | Audit work needs to strictly match rules and factual data, reducing randomness in generated content |
AIPROXY_API_ENDPOINT | Locally deployed ollama/vllm service address | Adapt to the local deployment needs of cross-departmental data calls within civil affairs systems |
AIPROXY_API_TOKEN | Access key for the corresponding service | Ensure the security of cross-system calls and prevent unauthorized access |
vision | Disabled | This scenario primarily processes text-based declaration materials, and does not require image recognition capabilities |
Three common configuration errors
- Phenomenon: A prompt reading "This is an image model, why is the image opening option not supported" appears during model testing. Cause: The
visionconfiguration item was accidentally enabled, but this scenario primarily processes text-based declaration materials and does not require image recognition capabilities. - Phenomenon: The model test returns a connection timeout after Docker is restarted. Cause: Ollama and FastGPT containers were not started in the correct order, resulting in a failed service link establishment.
- Phenomenon: The family income field is empty in the model return result. Cause: The
maxContextvalue is too small, truncating the content of the family economic status verification report that contains income data.
How to confirm the configuration is complete
- The model configuration interface of FastGPT v4.17.0 is accessed, and
visionis confirmed to be disabled, withmaxContextandmaxTokensvalues matching the business document length range. - A single low-income security application form and family economic status verification report are uploaded, a test call is initiated, and the return result is checked to include core fields such as family income and member relationships.
- After the Ollama and FastGPT containers are restarted, another model test is initiated, and service connections are confirmed to have no abnormal error reports.
- A cross-departmental data simulation call is configured, and the model is verified to correctly extract verification fields from multi-source data.
The values provided here are common starting points, and should be evaluated against applicable specific 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.