Reference only; verify against the target FastGPT release and deployment documentation.
What this type of data looks like
Data related to department budget preparation typically comes from the budget management integrated system, department staffing system, prior-year final accounts, project library, and Ministry of Finance budget preparation notices. Its update frequency is annual. Daily handled documents include department budget drafts, project application forms, performance target forms, personnel expense calculation forms, and budget approvals. These documents contain core fields such as unit name, project code, amount, performance indicators, and number of staff. Data formats and field naming vary slightly across different sources, and alignment is required before aggregation and analysis can be performed.
What constraints do these characteristics impose on model access and configuration
Annual updated data sources do not require frequent incremental synchronization, so full update configuration logic can be used. Multi-source heterogeneous data formats and field differences require access configuration to support custom field mapping rules, to avoid data misalignment. Single batches need to process summary requests for multiple subordinate unit documents, so context window configuration must adapt to long text input. The sensitivity of fiscal-related data requires access configuration to bind exclusive permissions, to restrict unauthorized access. Budget preparation compliance requirements also restrict model output randomness, to avoid content inconsistent with official standards.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single-batch summary of department budget-related documents needs to accommodate core fields from multiple application forms and calculation forms, requiring sufficient context capacity |
maxTokens | 4000–6000 token | Ledger aggregation and report generation need to cover multi-unit budget data, and output length must meet the requirements of fully presenting summary results |
temperature | 0.1–0.3 | Budget preparation must strictly match official standards and historical data, requiring reduced model output randomness to ensure content compliance |
AIPROXY_API_ENDPOINT | Corresponding internal network deployed proxy interface address | Adapt to the internal network isolation requirements of fiscal systems, and achieve secure connection with the budget management integrated system through proxy forwarding |
AIPROXY_API_TOKEN | Access token bound with exclusive permissions | Ensure access security of fiscal sensitive data and avoid data leakage |
toolChoice | auto | Automatically select whether to call internal data interfaces based on input content, adapting to the flexible needs of multi-source data aggregation |
Three common mistakes
- Using access configuration logic from general e-commerce platforms, resulting in 400 status code returns. The cause is failure to comply with the internal network isolation and data sensitivity requirements of fiscal systems, and direct use of non-compliant access methods.
- Setting
temperaturehigher than 0.5, leading to budget reports with inconsistent official standards. The cause is failing to match the strict compliance business requirements of budget preparation, and excessively relaxing model output randomness. - Failing to validate the
maxContextvalue, resulting in core fields such as project codes and number of staff being lost due to long document truncation. The cause is failing to adjust context length based on the field density of department budget documents, leading to missing critical data.
How to confirm the configuration is correct
- Initiate a test call for a single department budget draft, check if the returned result includes core fields such as project code, amount, and performance targets, with no obvious truncation or missing content.
- Access the configured
AIPROXY_API_ENDPOINTin the internal network environment, confirm there are no timeout or connection failure prompts, and verify that the proxy forwarding configuration is effective. - Adjust
temperatureto 0.2, generate two budget reports of the same type, check the consistency of standards and fields between the two outputs, and confirm that randomness control is effective. - View the call log, confirm that
toolChoiceautomatically triggers the aggregation logic based on the multi-source data identifier in the input, and verify that the automatic configuration is effective.
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.