What data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on model access and configuration
Declaration materials and files for nuclear power operations use fixed regulatory templates. They mix structured fields and long-text operation records, and include quantified monitoring data with units. This requires models to support both structured information extraction and long context understanding. Data updates incrementally with declaration cycles. Systems must adapt to high-frequency synchronous data source access logic to avoid review result deviations from delayed data sources. Uniform numbering rules and compliance field requirements demand models accurately match preset fields. This reduces unexpected field drift. Some documents are lengthy. Model context boundaries must be limited to avoid exceeding capacity limits.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long text length of nuclear power declaration documents, avoids truncating core compliance fields |
maxTokens | 3000–4000 token | Controls the length of single-round review output, matches the structured result output requirements of file reviews |
quoteMaxToken | 1500–2000 token | Limits the maximum length of referenced source documents, ensures accuracy of review basis |
toolChoice | auto | Supports automatic tool triggering, adapts to structured field extraction and quantified data verification |
temperature | 0.1–0.3 | Reduces randomness, ensures consistency of compliance review results, avoids unexpected field drift |
AIPROXY_API_TOKEN | Generate exclusive tokens based on intranet permissions | Adapts to the security isolation requirements of the nuclear power intranet, prevents public network data leaks |
Three common errors
- Phenomenon: After batch uploading declaration files, only the first dozen entries complete review. Subsequent entries have no results or return context overflow errors. Cause: The long text length of nuclear power documents is not adapted. The
maxContextvalue is too small, leading to insufficient context capacity. - Phenomenon: When the model calls functions to extract compliance fields, the result deviation rate is high. Field matching errors occur. Cause: The
temperaturevalue is too high, causing excessive randomness in model output. This does not meet the strict requirements of nuclear power reviews. - Phenomenon: jpg format file scan copies cannot be processed directly. No image analysis results are returned. Cause: The
visionconfiguration item is not enabled, and the pre-processing flow for image-to-text conversion is not configured.
How to confirm the configuration is properly set
- Upload a single standard nuclear power declaration document. Check whether the fields output by the model match the preset fields in the document one by one.
- Batch upload multiple files of the same type. Confirm that all entries complete review without abnormal errors.
- Adjust the
temperatureparameter. Compare the consistency of the two review results to confirm compliance with business strictness requirements. - Connect to the intranet-deployed model service. Verify that the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations are correct, with no access permission errors.
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.