What data for this category looks like
Data for this category comes from the budget management integrated system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. Daily processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Each document contains fields such as project number, fund amount, performance indicators, and supporting material links. There are no unified fixed units. Match field requirements to the corresponding business scenario.
Constraints imposed on model integration and configuration
The annual concentrated update rhythm requires model integration to support batch historical document synchronization. This avoids overload during single processing. Configure access formats for different systems to handle multi-source heterogeneous data. This ensures complete data extraction. Performance documents have strong field correlation. Model calls must retain the correspondence between projects, funds, and performance indicators. This prevents information fragmentation. Verification needs for supporting materials and performance targets require models to link cross-document business information. Configure reasonable context length parameters. This prevents key associated information from being truncated.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Performance evaluation-related documents are mostly long texts. Sufficient context must be retained to associate business information of projects, funds, and performance indicators |
maxTokens | 4000–6000 token | Performance evaluation reports must output complete verification conclusions and rectification suggestions. This avoids truncation of key content |
temperature | 0.2–0.4 | Performance evaluation requires rigorous conclusions. Overly divergent content should not be generated |
quoteMaxToken | 2000–3000 token | Sufficient supporting material reference content must be retained. This ensures the credibility and compliance of performance evaluations |
toolChoice | auto | Tasks can automatically select whether to call tools to verify supporting materials. This improves business processing efficiency |
AIPROXY_API_ENDPOINT | FastGPT exclusive API address deployed internally | This adapts to security access requirements for internal office work. It prevents sensitive fiscal data from being leaked |
Three Common Misconfigurations
- Phenomenon: An error is returned when calling the interface if the request message includes historical conversation content. Cause: The
maxContextparameter is not configured correctly, leading to context length exceeding the model's supported limit. Or the conversation history format is not properly spliced. - Phenomenon: The application returns results directly, instead of using them as knowledge base materials for secondary use by the large model. Cause: The application's direct return switch is not turned off. Or the tool call trigger condition is not configured, causing results to not enter the RAG process.
- Phenomenon: An external AI interface cannot be called normally after being integrated, and an interface exception is returned. Cause: The
AIPROXY_API_TOKENparameter is not configured correctly. Or the API address does not match the external interface format, and FastGPT's access specifications are not followed.
How to Confirm Correct Configuration
- Upload a single performance self-assessment form to initiate a test call. Check whether the returned content associates project, fund, and performance indicator information. Confirm that the context configuration meets business requirements.
- Initiate a test request that includes historical conversation content. Check whether results are returned normally without errors. Confirm that the context length configuration adapts to the business scenario.
- Adjust the
temperatureparameter and initiate a test. Check the rigor of the generated content. Confirm that the parameter configuration meets the business requirements of performance evaluation. - Call the configured API address. Check whether the returned result includes the expected verification conclusion. Confirm that the API access parameters are correct.
The values listed above are common starting points. They should be evaluated 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.