What the data for this category looks like
The core data for this category comes from the centralized treasury payment system, unified budget management system, electronic invoice platform, and contract management system. Daily circulation includes payment applications, fund use plans, invoices, contracts, acceptance receipts, reimbursement approval forms, payment vouchers, and other documents. Data is updated once per day. Each document includes fields such as budget indicators, economic classification, payment amount, approval nodes, and acceptance information. All fields must strictly match the standard format of fiscal systems, with no options for custom expansion.
What constraints these characteristics impose on model access and configuration
Reference only; verify against the target FastGPT release and deployment documentation.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-source splicing needs of fiscal documents, avoiding loss of core verification information such as budget indicators and contract clauses due to context truncation |
maxTokens | 2000–3000 token | Controls the output length of individual audits, ensuring returned audit opinions are clear and comply with fiscal audit format requirements |
temperature | 0.1–0.3 | Reduces output randomness, ensuring consistent and rigorous conclusions for compliance audits, matching the rigid specifications of fiscal business |
AIPROXY_API_ENDPOINT | Fill in dedicated nodes according to the deployment environment | Adapts to the network environment requirements of multi-source data docking, ensuring stable interaction with fiscal systems |
AIPROXY_API_TOKEN | Bind dedicated service keys | Restricts access permissions to avoid unauthorized calls affecting the security of fiscal data |
Three common configuration mistakes
- Phenomenon: The agent execution details page shows "Model response completed reason: connection disconnected". Cause: Unreasonably configured
maxContextleads to overly long context, triggering the connection timeout limit of the model service. - Phenomenon: After accessing a specified large model, output results under the same prompt differ significantly from those of the native platform. Cause: The
temperatureparameter was not adjusted, resulting in overly high randomness of model output that does not meet the rigorous requirements of fiscal audits. - Phenomenon: Some fields are missing during batch processing of daily data. Cause:
maxTokenswas not limited, causing the model to lose key field information from some fiscal documents during long context processing.
How to confirm the configuration is complete
- Call the test interface, pass in combined data of a single reimbursement approval form and invoice, check whether the budget indicators and economic classification fields extracted by the model match the source data, and adjust parameters until the matching degree meets business requirements.
- View the agent execution logs, confirm that the response time of model calls meets business expectations, and there are no frequent timeouts or disconnections, to verify connection stability.
- Test context processing after multi-source data splicing, confirm that no key information is missing due to content truncation, and adjust the
maxContextparameter until requirements are met. - Check the API key and endpoint configuration, confirm that only authorized services can call the model interface, to verify the security of access permissions.
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.