What Data for This Category Looks Like
This category's data mainly comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Daily produced documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. The data update frequency is annual. Most document fields include performance indicator items, fund allocation details, output effect quantification items, and rectification measure items. There are no unified fixed units, and the actual business fields of the corresponding project must be matched.
What Constraints These Characteristics Impose on the "Tool Calling and Plugins" Link
Data for this category is updated annually and sourced from multiple dispersed systems. This requires tool calling plugins to support scheduled synchronization of multi-system data sources to avoid manual repetitive data entry. Document types are diverse and fields have no unified standards, so plugins must adapt to field mapping rules for different document templates. The core links of performance evaluation rely on association verification between supporting materials and fund data. Tool calling needs to support batch pulling of payment ledgers and satisfaction data for corresponding projects to reduce manual verification workload. At the same time, the output of tool calling must comply with official document format specifications to avoid project score reductions or budget cuts due to format errors.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to automatic tool calling requirements for multiple document types, no manual tool specification needed |
functionCall | Enabled | Forces the model to return tool calling parameters in structured format, facilitating parsing of structured fields in performance evaluation reports |
| Stop sequences | ["\n\n", "### Rectification Recommendations"] | Matches common paragraph separators in performance evaluation reports to avoid generating redundant content |
| Maximum context window | 8000–12000 token | Adapts to context length for annual project data and multiple documents, calibrated based on actual testing |
| Single tool call limit | Top 3 entries | Controls the number of supporting materials pulled in a single call to avoid context overload |
| Timeout duration | 120 seconds | Adapts to time requirements for pulling multi-source data, prevents call failures due to large data volumes |
Three Common Mistakes
- Issue: Base64-formatted supporting images embedded in tool call-generated performance evaluation reports fail to render properly. Cause: Base64 support for markdown rendering was not enabled in plugin configurations, or the generated base64 string was not properly wrapped in markdown image syntax.
- Issue: Tool call-generated content exceeds expected paragraphs and includes undefined redundant text. Cause: Stop sequence configurations do not match common paragraph separators in performance evaluation reports, causing the model to terminate generation at an incorrect position.
- Issue: 404 errors are returned when calling external evaluation tools. Cause: The API address or model name configured for the tool does not match the versions supported by the platform, and dedicated parameters for this category were not correctly filled in.
How to Confirm Configurations Are Correct
- Initiate a test for generating a performance evaluation report for a single annual project, check if the generated content includes complete performance indicators, fund details, and rectification recommendations.
- View tool call logs to confirm that multi-source data has been correctly pulled and associated with the business fields of the corresponding project.
- Test embedding base64-formatted supporting images, confirm that they render properly in the FastGPT preview interface.
- Check that the stop sequence matches the paragraph separators in the generated content, confirm that the generated content does not exceed the expected document structure.
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.