What this category of data looks like
This category of data covers equipment ledgers, inspection forms, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Sources include EAM/CMMS systems, device sensor data, operator inspection records, maintenance history databases, spare parts inventory systems, and statutory inspection information. Data update cycles include real-time sensor collection, shift-by-shift inspection submissions, daily/weekly/monthly maintenance plan updates, and work orders and analysis documents generated immediately when a fault occurs. Data fields include unique device identifiers, inspection item parameters, fault types, spare part codes, inspection validity periods, and formats are adjusted based on device type and workshop management rules.
What constraints do these characteristics impose on model connection and configuration
The decentralized sources, multiple update cycles, and field differences of this category of data impose multiple constraints on model connection and configuration. Multi-source heterogeneous data formats require configuration of parsing rules adapted to different structures to ensure that device sensor data, inspection records, and work orders can be read correctly. Data sources with different update frequencies require differentiated trigger synchronization mechanisms. Real-time sensor data needs configuration of frequently refreshed context loading rules, while monthly maintenance plans can be synchronized on a fixed cycle. The data includes special equipment inspection records that require associated verification, so the data source range for model input must be limited to avoid irrelevant information interfering with fault analysis. The association between work orders and fault analysis relies on field matching, so the priority and matching logic of associated fields must be clearly defined.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category of data includes multiple inspection records, work orders, and sensor data, requiring sufficient historical context to complete cross-document quality traceability, and adapted to the context loading rules of FastGPT v4.17.0 |
maxTokens | 2000–3000 token | Fault analysis and traceability results need to include content such as fault causes and handling suggestions, and do not require overly long output to avoid excessive consumption of inference resources |
temperature | 0.2–0.4 | Quality traceability and defect analysis require accurate results, with no excessive randomness, to ensure consistency of analysis conclusions |
AIPROXY_API_ENDPOINT | Exclusive access address corresponding to the cloud platform | Adapted to the network environment and model call path of private deployments, ensuring that requests can be correctly routed to the target model |
AIPROXY_API_TOKEN | Valid token string obtained from a third-party platform | Used for identity verification during model calls, ensuring legal and valid access permissions |
| Workflow/API tool selection | auto | Automatically select whether to call the knowledge base or tools based on tasks, adapted to multi-source data retrieval and associated analysis requirements |
Three common mistakes
- Phenomenon: After adding a tested and approved model on the model provider page, a 404 error occurs during testing. Cause: The model ID consistent with the third-party platform is not filled in, or the proxy API endpoint configuration is incorrect.
- Phenomenon: After connecting the specified large model, the output result has no thinking process. Cause: The
temperatureparameter is set too high, or the output rule for associated analysis is not enabled in the model configuration. - Phenomenon: After private deployment, there is a significant difference in model inference efficiency compared to the platform version. Cause: The
maxContextparameter value exceeds the range supported by the local server, leading to increased context loading time.
How to confirm successful configuration
- Run the test connection function on the model provider page to confirm that no errors are returned in the result.
- Import a single piece of equipment inspection data and its associated work order, trigger a model call, and verify that the output result includes relevant fault analysis content.
- Adjust the
maxContextparameter value, observe the change in context loading time, and confirm that the value is adapted to the resource configuration of the current deployment environment. - Check that the
AIPROXY_API_TOKENmatches the token generated by the third-party platform, to ensure that the identity verification configuration is correct.
The values provided are common starting points and should be measured against the reader's own samples.