What this category of data looks like
Internal data sources for nuclear power operations include reactor operation logs, equipment ledger records, on-site inspection records, safety operation specifications, and employee training assessment archives. Update frequency varies by data type: operation logs are generated per shift, equipment ledgers are updated with quarterly overhauls and maintenance, and safety regulations are revised annually. Most documents use a structured combined format. Equipment ledgers include fields such as equipment number, rated parameters, and maintenance cycle. Inspection records include inspection points, detection values, and anomaly markers, using engineering professional units such as MPa, ℃, and hours.
What constraints these characteristics bring to the "Model Integration and Configuration" link
Nuclear power operation data contains many structured fields with engineering units and real-time generated time-series logs. This requires models to accurately identify units and associate context information. Real-time operation logs are updated frequently, so the configuration must support dynamic loading of recent data and avoid context overflow. Equipment ledgers and safety regulations have long document structures, so the configuration must adapt to long-text splitting and accurate reference. Mixed input of multi-source heterogeneous data requires the configuration to clearly define data association rules, to avoid response deviations caused by field confusion.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context retention needs of multi-turn conversations and recent shift operation logs in nuclear power scenarios, avoiding loss of key information |
maxTokens | 4000 token | Ensures that single-turn replies can fully cover engineering content such as equipment parameters and operation steps, avoiding truncation of key instructions |
quoteMaxToken | 2000 token | Supports complete reference of structured fields and detection values from inspection records and equipment ledgers, retaining original engineering units and markers |
temperature | 0.3–0.5 | Aligns with the rigor requirements of nuclear power scenarios, reducing randomness of generated content and avoiding results that do not comply with engineering specifications |
AIPROXY_API_ENDPOINT | Proxy address deployed on the enterprise intranet | Ensures that sensitive nuclear power data does not leave the intranet, complying with internal data security specifications |
AIPROXY_API_TOKEN | Exclusive access key assigned by the enterprise | Restricts unauthorized access to internal model services and improves interface access security |
Three common errors
- A context truncation error occurs when initiating a test request, and the reply does not include the previously mentioned inspection sub-question. The cause is that the value of
maxContextis too small to accommodate historical information and associated data in multi-turn interactions. - The interface call returns an authentication failure status code, and the model service cannot be called normally. The cause is that
AIPROXY_API_TOKENis not filled in correctly, or an invalid access key is used. - The generated operation recommendations include parameter values that do not comply with engineering specifications. The cause is that the value of
temperatureis too high, leading to a lack of rigor in the model's generated content.
How to confirm the configuration is complete
- Initiate a test request that includes historical conversations and equipment parameters, and check whether the reply associates the previously mentioned inspection points and equipment numbers.
- View the interface call logs to confirm that the request completed authentication using the configured
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. - Adjust the
temperatureparameter and initiate a test, then check whether the rigor of the reply complies with engineering scenario requirements. - Input long-text format safety regulation content, and check whether
maxContextandmaxTokensnormally accommodate complete data with no truncation prompts.
The values provided are common starting points and should be evaluated 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.