What the data for this category looks like
Data sources for this category include nuclear power operation work orders, on-site work procedures, approval process ledgers, and compliance inspection records. Update schedule: Work order data is synced in real time. Procedure documents are updated quarterly or as required by compliance regulations. Document structure includes structured work order fields (process ID, node name, approval time limit, responsible team) and unstructured procedure text (step descriptions, safety requirements). Field units are typically hours, items, copies, team numbers, and similar. There are no vague fields common to general business scenarios.
What constraints these characteristics impose on model access and configuration
The above data characteristics impose multiple constraints on model access and configuration. Structured work order data requires clear field mapping rules to avoid deviations in process node matching. Unstructured long procedure text needs sufficient context processing capability to prevent key steps from being truncated. Frequently updated work order data requires configured scheduled synchronization tasks to ensure real-time data source availability for model calls. Nuclear power business compliance requirements strictly limit model output deviations, while ensuring sensitive data is not leaked. Fixed business process nodes require models to prioritize matching preset process logic when called, to adapt to standardized handling processes.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Nuclear power business procedures are mostly long texts, requiring space for complete process steps and compliance clauses |
quoteMaxToken | 2000–3000 characters | Requires complete quoting of work order nodes and procedure fragments to ensure the model can recognize valid matching content |
temperature | 0.1–0.3 | Business process guidance requires precise output to avoid deviations in process steps |
toolChoice | auto | Requires automatic invocation of process retrieval tools to match fixed nuclear power business processing logic |
AIPROXY_API_ENDPOINT | Fill in the dedicated node according to the deployment environment | Meets compliance access requirements of nuclear power systems, isolating unauthorized calls |
AIPROXY_API_TOKEN | Bind dedicated service keys | Ensures secure identity verification for API calls, complying with nuclear power data security specifications |
Three common configuration mistakes
- Symptom: A "context limit exceeded" error is returned when running the process guidance workflow, with status code 413 displayed on the interface. Cause: The
maxContextparameter was not adjusted for nuclear power long documents, and the default value is too small to accommodate complete business procedure fragments. - Symptom: The semantic retrieval matching score of the knowledge base is 0.77, but the model prompts that no relevant information is available. Cause: The
quoteMaxTokensetting is too short, and the retrieved procedure fragments are truncated, making them unrecognizable as valid matching content by the model. - Symptom: The workflow response times out. Cause: The
maxTokenssetting is too large, exceeding the default time limit of API calls, and does not match the real-time response requirements of nuclear power business.
How to verify the configuration is complete
- Upload a single nuclear power business procedure document, and verify that the process nodes returned by the model fully correspond to the preset fields in the document.
- Simulate submission of business work order data, and verify that the processing steps output by the model match the approval link configuration of the work order.
- Trigger the scheduled synchronization task, and verify that the system updates the nuclear power business data source at the preset rhythm.
- View the API call logs, and confirm that the call requests for
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTboth return valid results successfully.
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.