What This Category of Data Looks Like
This category of data mainly comes from the "One Task" special zone of the National Government Service Platform, local integrated government service platforms, electronic certificate repositories, and approval systems of various departments. Daily maintained documents include co-handling task lists, one-time notification sheets, co-handling application forms, material circulation sheets, parallel approval opinions, and completion receipts. Data is updated daily. Documents typically contain fields such as cross-departmental task association rules, application material requirements, and processing flow nodes. There is no unified fixed unit, and values adjust based on task type.
Constraints Imposed on Model Access and Configuration by These Data Characteristics
Decentralized data sources and daily updates require model access to support dynamic pulling of the latest government platform data. Configuration must adapt to format conversion for multi-source data. Diverse document types require the model to recognize field logic across different documents. Context length parameters must be adjusted specifically during configuration. Association rules for cross-departmental tasks are complex. Model calls need sufficient context support, while token consumption must be controlled to avoid exceeding limits. The rigor of government data requires strict control of output content compliance during configuration, to prevent Q&A results that do not meet official requirements.
How to Set Configuration
The following configuration items apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Co-handling tasks involve multi-department documents, requiring sufficient context to cover cross-department rules and material requirements |
maxTokens | 2000–3000 token | Policy Q&A needs to fully output basis content while controlling single-call costs |
quoteMaxToken | 1500 token | Official document fragments must be prioritized for reference, limiting the length of referenced content to ensure accuracy |
toolChoice | auto | Need to automatically select whether to call tools to obtain the latest co-handling data based on user questions |
temperature | 0.1–0.3 | Policy Q&A requires rigor to avoid generating statements that do not meet official requirements |
AIPROXY_API_ENDPOINT | Fill in the formal address provided by the channel | Adapt to compliant API call paths in government scenarios |
Three Common Configuration Mistakes
- Phenomenon: An error is reported after clicking when the model channel is configured. The call log shows abnormal data acquisition. Cause: The latest authorization credential provided by the channel is not used, or
AIPROXY_API_TOKENis filled incorrectly. - Phenomenon: A 304 status code is returned when switching model channels on the local front end. Cause: The locally cached API address configuration is not refreshed, or the latest authorization information is not carried in the request header.
- Phenomenon: Large models that only support stream mode cannot be called normally. Cause: The streaming return logic is not adapted in the model configuration, or the
maxContextparameter is not adjusted correctly to adapt to streaming content splicing.
How to Verify Successful Configuration
- Manually enter a test question related to co-handling policies, trigger a model call, and check whether the returned result references official document fragments.
- View the model call log to confirm that the request parameters of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare consistent with the configured content. - Switch test questions for different co-handling tasks to verify whether the rigor of the output content meets the requirements of government Q&A.
- Simulate a streaming call scenario, check whether the model can return segmented content according to the configured mode, without obvious content truncation.
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.