Reference only; verify against the target FastGPT release and deployment documentation.
What Data Looks Like for This Category
Data related to nuclear power operation document and material drafting mainly comes from unit operation logs, safety review reports, maintenance work procedures, equipment ledgers, and approval circulation documents. Data update cadence covers daily and special scenarios: operation logs are updated daily, special safety reports are updated monthly or at special task nodes, and maintenance records are archived immediately after project completion. All documents follow nuclear power industry standardized formats, and include fixed fields such as unit number, event timestamp, responsible team, and equipment model. Units include megawatts, operating hours, sets, and other professional metrology identifiers.
What Constraints These Characteristics Impose on the Model Access and Configuration Link
The standardized format and fixed field requirements for nuclear power documents mean the model must accurately recognize professional terminology and structured information. As a result, the model context capacity must match the length of individual documents. Multiple dispersed data sources require the access link to support batch import and synchronization of different file types. It also requires matching data update frequencies to configure automatic refresh rules for the knowledge base. The strictness requirements of professional scenarios limit the range of randomness parameters during model generation, to avoid expressions that do not comply with industry specifications.
How to Set Configurations
| Configuration Item | Suggested Value | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the thousands-of-character length of individual nuclear power documents, avoids context truncation that loses critical information |
maxTokens | 4000–6000 token | Meets the output length requirements for document drafting, while not exceeding the output limits of most models |
temperature | 0.2–0.4 | Reduces generation randomness, ensures the strictness and compliance of nuclear power documents |
toolChoice | auto | Allows the model to independently judge whether to call knowledge base tools to obtain the latest unit operation data |
AIPROXY_API_ENDPOINT | Fill in the local access address of the privately deployed model | Adapts to the call requirements of the local private Qwen3-32B model, replaces public model endpoints |
AIPROXY_API_TOKEN | Fill in the access key for the corresponding private model | Verifies model call permissions, avoids interface errors caused by unauthorized access |
Three Common Misconfigurations
- Phenomenon: Local private Qwen3-32B model calls return 401 or 500 errors, and calls cannot be completed. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the endpoint address and key do not match the actually deployed model. - Phenomenon: Generated draft documents do not include uploaded nuclear power maintenance record content, and the returned results lack corresponding fields. Cause: The context of uploaded files is not associated during the model access link, or the knowledge base content recall logic is not enabled.
- Phenomenon: Knowledge base documents still cannot be recalled after configuration, and generated results deviate from professional requirements. Cause:
maxContextis set too small, so the context window cannot hold enough recalled documents, and critical reference information is lost.
How to Confirm Configurations Are Correct
- Initiate a test call, input a typical prompt for nuclear power operation and maintenance document drafting, check if the returned results include preset professional terminology and standardized formats.
- View model call logs, confirm that the request parameters for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly carried, and there are no 401, 404, or 500 level errors. - Upload a nuclear power maintenance record file, initiate a drafting call, check if the returned results are associated with the specific content of this file.
- Adjust the
temperatureparameter to 0.3 and call again, observe the change in the strictness of the returned results, and confirm that the parameter configuration has taken effect.
The values provided here 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.