What the data for this category looks like
Data for this category comes mainly from the integrated budget management system, project application materials, fund payment data, business system ledgers, and satisfaction surveys. Update data on an annual cycle. Daily output includes performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Most document structures include performance indicator details, fund usage related entries, supporting material association identifiers, and rectification item list fields. No fixed unified format template exists. Adjust documents based on actual project conditions.
What constraints these characteristics impose on the "model access and configuration" link
Annually updated data scale fluctuates. Configure parameters adapted to different context lengths. Multi-source heterogeneous data requires the model to accurately associate fund expenditures with performance targets. This imposes high requirements for the logical rationality of tool calls. Unfixed document formats require the model to have flexible text parsing capabilities. Verify supporting materials using cross-referenced multi-source data. Configure reasonable context reference rules. Performance evaluation reports have formal content requirements. Generated content must comply with official document specifications. Restrict excessive generation freedom.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the total context length of annual project documents, covering multi-source data and report writing needs |
maxTokens | 2000–3000 token | Meet the generation length requirement for a single performance evaluation report, avoiding output truncation |
quoteMaxToken | 1500–2000 token | Limit the total length of referenced supporting materials and business data, ensuring context focus |
toolChoice | auto | Allow the model to independently judge whether to call tools to obtain fund data or supporting materials, adapting to multi-source data association needs |
temperature | 0.3–0.5 | Balance the rigor and flexibility of generated content, meeting the formal requirements of performance reports |
AIPROXY_API_TOKEN | Fill in the valid token provided by the selected model service provider | Complete identity verification for model calls, ensuring request legality |
Three common mistakes
- A
model stream response is emptyerror occurs. The cause is that themaxContextparameter is set too small, failing to load complete annual project data, resulting in the model being unable to generate valid output. - An error occurs when calling multimodal models to process image-based supporting materials, with images unable to be downloaded. The cause is that appropriate network access parameters are not configured, preventing the model from obtaining external supporting image resources.
- Fixing tool call rules without setting to
autocauses the model to fail to flexibly associate multi-source data based on actual business needs, leading to rigid performance evaluation logic.
How to confirm the configuration is complete
- Submit complete data for a single annual project, check if the model-generated performance self-assessment form covers all core fields with no obvious truncation.
- Trigger a tool call test, confirm that the model can autonomously call tools to obtain corresponding fund payment data based on input performance indicators.
- Review the model-generated performance evaluation report, confirm that the referenced supporting material information matches the input data with no logical conflicts.
- Test input of project data of different scales, confirm that the
maxContextparameter can adapt to data volume fluctuations with no context overflow errors.
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.