What the data for this category looks like
This category's data primarily originates from the budget management integrated system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. Daily documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Data fields cover project performance indicators, fund usage details, output effect records, and rectification status. There are no unified fixed units. Field rules must be matched to specific projects.
How these characteristics impose constraints during model access and configuration
The annual update feature means the knowledge base sync cycle can be set to annual, without requiring high-frequency updates. However, the number of documents recalled in a single query may be large. Context length must be limited to adapt to model processing capabilities. Multi-source data includes structured budget management data and unstructured supporting materials. Parsing rules adapted to different data sources must be configured to ensure correct recall of content in all formats. Multiple document types require clear recall scope to avoid irrelevant documents interfering with the accuracy of policy basis Q&A. Project-related fields have no unified units. The content recalled by the model must match the field definitions of specific projects. The length of single quoted documents must be limited to avoid exceeding model processing limits. The FastGPT v4.17.0 configuration interface must be adjusted to accommodate these constraints.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length after multi-document recall in the fiscal expenditure performance evaluation scenario, avoiding exceeding the context limits of mainstream large models supported by FastGPT v4.17.0 |
maxTokens | 2000–3000 token | Ensures generated policy basis Q&A responses are sufficiently complete, while controlling per-call costs |
quoteMaxToken | 1500–2000 token | Limits the length of single quoted documents, avoiding overly long quoted content interfering with the model's accurate extraction of policy basis |
temperature | 0.1–0.3 | Reduces randomness in generated content, ensuring policy basis Q&A responses meet the rigor requirements of official documents |
toolChoice | auto | Automatically selects whether to call retrieval tools, adapting to the fiscal expenditure performance evaluation scenario with multi-document recall |
AIPROXY_API_ENDPOINT | Fill in the official address provided by the platform | Ensures connectivity for model interface calls, avoiding call failures caused by incorrect configuration |
AIPROXY_API_TOKEN | Fill in the key assigned by the platform | Completes identity verification for model interface calls, ensuring normal call permissions |
Common Configuration Errors
- The symptom is that the model recalls multiple irrelevant documents and cannot accurately locate the specified performance evaluation report. The cause is that no recall rules for specific documents have been configured, leading the recall scope to cover the entire knowledge base.
- The symptom is an interface returning a 429 status code, with current-limiting errors triggered during concurrent calls. The cause is that the call frequency for the fiscal expenditure performance evaluation scenario has not been adapted, and reasonable concurrency control has not been set in the configuration.
- The symptom is generated responses containing policy basis deviations and insufficiently rigorous expression. The cause is that
temperatureis set too high, causing the model's generated content to deviate from the rigor requirements of official documents.
How to Confirm Successful Configuration
- Initiate a query targeting a specific performance evaluation report, and verify whether the recall results only include relevant content from the target document.
- Conduct a multi-concurrency test, and confirm that no current-limiting errors are triggered during interface calls.
- Generate a test response, and verify whether the rigor of the content meets the requirements of policy basis.
- Check whether the configurations for
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTmatch the official information provided by the platform.
The provided values are common starting points, and should be measured against samples relevant to the specific use case.
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.