What Data Looks Like for This Category
Core data for this category comes from the national primary and secondary school student academic records management system, local enrollment platforms, household registration and property data, and school academic affairs systems. The update frequency is daily. Commonly processed documents include enrollment application forms, student academic records information forms, transfer applications, enrollment plans, admission rosters, and student status change approval forms. The core fields of document structures focus on student identity, academic records status, and associated supporting materials. No custom units are used, and most fields are standardized identity, address, and date-related content.
What Constraints These Characteristics Impose on Model Access and Configuration
Data sources for this category are scattered, so model access must support unified parsing of cross-source standardized formats. Daily updated data sources require configured call frequencies and refresh rhythms to align with the business cycle. Documents include multiple associated materials, so models must have context association verification capabilities. The standardized nature of fields also requires model output to strictly match preset formats to avoid non-standardized conclusions. Errors in the review process directly affect student enrollment and academic records status, so configuration must balance accuracy and response speed.
How to Set Configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Enrollment review requires association of multiple materials; sufficient context ensures accuracy of cross-document verification |
maxTokens | 2000–3000 token | Each review conclusion must clearly output verification results and supporting basis to avoid truncation of critical information |
vision | Enabled | Application materials include image-based proofs such as household registration books and property ownership certificates; visual parsing capability is required |
temperature | 0.1–0.3 | Reviews must strictly follow rules; reducing randomness ensures consistency of conclusions |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the vendor | Adapt to access paths of different model vendors to ensure proper routing of requests |
AIPROXY_API_TOKEN | Fill in the valid key issued by the vendor | Complete identity verification to prevent unauthorized model calls |
Three Common Mistakes
- After uploading image materials such as household registration books and property ownership certificates, the model fails to return image analysis results, or returns empty image analysis fields. Cause: The
visionconfiguration item is not enabled, or a multimodal model channel is not selected. - After configuring a thinking model, garbled content is returned; no exceptions occur when only using non-thinking models. Cause: The prompt format of the model is not matched, or the configured prompt template is incompatible with the model version.
- When creating a knowledge base or uploading materials, no multimodal model options are available. Cause: The installed FastGPT version does not support multimodal model access, or multimodal support is not enabled in the channel configuration.
How to Confirm Configuration Is Complete
- Upload a single image-based application material to trigger model parsing, and check whether a text extraction result of the image content is returned.
- Submit a review request containing multiple associated materials, and check whether the model output covers verification conclusions for all associated fields.
- View the added model vendors on the channel configuration page, and confirm that the access configuration of the target vendor has been saved and the status is normal.
- Adjust the
temperatureparameter to the range suitable for the review scenario, submit duplicate review requests, and check whether the two output verification conclusions are consistent.
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.