What data looks like for this category
The core data for this category comes from the budget management integrated system, department staff establishment system, previous year final accounts, project library, and Ministry of Finance budget preparation notices. The update frequency is annual. Daily processed documents include department budget drafts, project application forms, performance target forms, staff expense calculation forms, and budget approvals. Each document contains fixed fields: project code, unit name, expense amount, staff establishment number, performance target, etc. These fields must match the unified format specifications of the finance department, with no additional custom fields.
What constraints do these characteristics impose on the "model access and configuration" link
The annual centralized update feature requires declaration materials and file review to process large volumes of batch data within a fixed cycle, creating clear requirements for model concurrent processing capabilities. Multiple data sources require models to align field formats across different systems to avoid extraction deviations. Documents have fixed structures but a large number of fields, so models must accurately locate key information required for review and filter irrelevant content. In addition, review results must comply with finance department compliance requirements, and model output must strictly match preset fields with no format or content deviations.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Department budget documents have large single-batch content volume, requiring complete loading of context from multiple declaration materials and files |
temperature | 0.1–0.3 | Review scenarios require stable, compliant output to avoid random deviations affecting review judgments |
quoteMaxToken | 1500 characters | Precise reference to key fields from original declaration materials is required, and reference length is controlled to ensure clear review logic |
AIPROXY_API_ENDPOINT | Fill in according to actual deployment node | Adapts access paths for local or cloud models to ensure normal connection |
toolChoice | "none" | Review scenarios do not require calling external tools, focusing on text extraction and review judgments |
Three common errors
- Calling a locally deployed Ollama model returns not found or permission errors. The cause is that local model access configuration is not correctly added to
.env.local, orAIPROXY_API_TOKENdoes not match the verification rules of the model platform. - Incorrect units appear in extracted expense fields. The cause is that pre-validation for standard units of budget documents is not performed, and the model fails to match the unified format specification of the data source.
- Response timeout occurs when batch processing multiple application forms. The cause is that
maxContextis set beyond the context limit supported by the model, leading to processing delay.
How to confirm configuration is complete
- A single staff expense calculation form is imported, content extraction is triggered, and the extracted staff establishment number and expense amount fields are verified to match the original document.
- Model call logs in FastGPT v4.17.0 are reviewed to confirm that
AIPROXY_API_ENDPOINTmatches the actual deployment path, with no connection errors. - The
temperatureparameter is adjusted, multiple samples are tested, and the output results are confirmed to have no obvious random fluctuations. - Multiple identical types of declaration materials are batch imported, the processing process is checked for no interruptions, and full context loading is confirmed.
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.