This page applies to FastGPT v4.17.0.
What Data for This Category Looks Like
This category of data comes from daily outputs and system storage of manufacturing enterprise equipment departments, workshop maintenance teams, and public utility departments. It includes documents such as equipment ledgers, inspection checklists, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Data sources cover EAM/CMMS systems, equipment sensor collection, operator inspection records, maintenance history archives, spare parts inventory data, and statutory inspection information. Update frequencies include shifts, daily/weekly/monthly cycles, and immediate updates when faults occur. Document fields include equipment number, rated power, inspection points, measured values, maintenance cycles, fault codes, and some fields have unit identifiers.
What Constraints These Characteristics Impose on the Model Access and Configuration Link
Multi-source, multi-update-rate data requires configuration that supports cross-system data pulling and incremental synchronization adaptation. Mixed processing of multiple types of documents requires configuration of parsing rules compatible with different formats, to ensure accurate field recognition for structured documents such as inspection checklists and work orders. Unit identifiers attached to fields require the model to accurately match the units of corresponding values, to avoid parameter mismatch issues during cross-review. The high-risk nature of core business links requires configuration to prioritize consistency and accuracy of review results, reducing risks such as unplanned downtime and safety accidents.
How to Set the Configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Equipment-related documents include multiple types of long text such as ledgers and work orders, requiring sufficient context to complete cross-document cross-checks |
maxTokens | 2000–3000 token | The core information length of a single inspection checklist or maintenance work order is moderate, avoiding redundant output that affects review efficiency |
quoteMaxToken | 500 token/entry | Specific field content of equipment ledgers and spare parts lists must be accurately referenced, controlling reference length to ensure readability |
| Workflow/API tool selection | auto | Document parsing and data query tools must be called automatically based on review tasks, adapting to complex scenarios of multi-document cross-checks |
temperature | 0.1–0.3 | Technical document review requires high consistency and accuracy, reducing the risk of parameter matching errors caused by random output |
AIPROXY_API_TOKEN | Fill in the key generated during deployment | Complete interface identity verification to ensure call security |
Three Common Mistakes
- Symptom: A prompt indicating that the thinking switch is not supported is returned when calling Qwen3 series models. Cause: The dedicated thinking mode parameter for the corresponding model was not enabled in the model access configuration, causing the model to fail to trigger preset thinking logic.
- Symptom: Generic prompt text is returned when parsing Excel-format inspection checklists. Cause: Table recognition rules for document parsing were not configured correctly, causing table fields to fail to be fully extracted, so the model cannot obtain valid review basis.
- Symptom: Tool call functions cannot be used after a locally deployed model is accessed. Cause: Tool call permissions were not enabled in the configuration, or the model endpoint was not correctly mapped to the function call interface, causing the model to fail to trigger tool calls.
How to Confirm the Configuration Is Complete
- An operator uploads a single equipment inspection checklist and the corresponding equipment ledger, triggers a cross-review task, and checks whether the output content accurately references specific fields and values from the documents.
- An administrator calls the test interface, reviews the returned logs to confirm they contain correct model call parameters and document parsing results, and verifies that the parameter configuration has taken effect.
- An operator simulates the document submission scenario when a fault occurs, and verifies whether the model can quickly locate associated maintenance history and spare parts inventory information.
- An administrator checks whether the API endpoint and key on the configuration page match the deployed model service, to avoid call exceptions caused by identity verification failure.
The values provided here are common starting points, and should be evaluated against local deployment samples.