What data for this use case looks like
Data for this use case primarily comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. The update frequency is annual. Commonly processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Data fields include project identifiers, fund disbursement nodes, performance indicator corresponding items, supporting material association paths, and more. Overall, this is an annually updated dataset that mixes batch-structured and unstructured data.
Constraints imposed by these characteristics on model integration and configuration
The mixed, annually batched update data characteristics require model integration and configuration to support multi-document type parsing and batch context association. Data from multiple sources must adapt to different document parsing formats, especially supporting materials with embedded images, which require visual understanding capabilities. The annual update feature does not require high-frequency incremental synchronization, but the volume of documents processed in a single operation is large, so the single context length must be limited to avoid overflow. Additionally, the rigor of performance evaluation requires stable model output, so configuration must balance efficiency and accuracy, and must integrate with the fiscal system’s identity verification to meet compliance requirements.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multiple annual batch-processed performance documents, retains complete context association for self-assessment forms, supporting materials, and rectification reports |
vision | Enabled | Processes embedded images in performance reports and supporting materials, such as acceptance photos, scanned payment vouchers, and other unstructured visual information |
temperature | 0.2–0.4 | Ensures rigorous wording for performance scoring and rectification suggestions, reduces subjective bias, and complies with fiscal evaluation compliance requirements |
AIPROXY_API_ENDPOINT | Fill in local or public cloud endpoints based on the deployment environment | Adapts to the need for calling locally deployed large models, meets the data local deployment requirements of some institutions |
AIPROXY_API_TOKEN | Fill in the actual generated key | Implements identity verification, supports blocking anonymous access, and enables secure integration with external fiscal systems |
maxTokens | 4000–6000 token | Adapts to the generation length of a single performance evaluation report, avoids accidental truncation of output content |
Three common configuration errors
- Model call tests pass but the model does not appear in the FastGPT backend: The phenomenon is that the target configured model does not show up in the model management page, and the interface self-test returns success but there is no record in the backend. The cause is that
AIPROXY_API_TOKENor the endpoint address was not filled correctly, leading to identity verification failure and incomplete model mounting synchronization. - Embedded images cannot be recognized by the model after document parsing: The phenomenon is that after uploading supporting materials containing images, the model output does not mention the image content. The cause is that the
visionconfiguration item was not enabled, so visual understanding capabilities are not activated, and supporting information in the images cannot be read. - No valid response after external system API call: The phenomenon is that empty data or an error is returned after the frontend sends a request. The cause is that API parameters were not packaged correctly, or the FastGPT conversation interface format was not matched, causing the request to fail to be parsed correctly.
How to confirm the configuration is complete
- Enter the FastGPT v4.17.0 model management page, check if the target model exists in the configured model list, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare filled correctly. - Upload a performance supporting material containing embedded images, initiate a parsing test, and confirm that the model can return results including descriptions of the image content.
- Call the external system’s API interface, pass test performance ledger data, and check if a summarized report fragment can be returned normally.
- Configure access permission policies, verify that anonymous users cannot access the model configuration page and conversation interface, and confirm that identity verification is effective.
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.