What data for this category looks like
Data for this category originates from EAM/CMMS systems, equipment sensor terminals, operator inspection records, maintenance history archives, spare parts inventory lists, and statutory inspection records for special equipment. Daily documents include equipment ledgers, inspection forms, maintenance plans, work orders, fault analysis reports, spare parts lists, and inspection records. Document fields cover equipment numbers, inspection locations, parameter thresholds, maintenance times, spare part models, and other details. Units include specific measurement types such as degrees Celsius, millimeters, and hours. Data update frequencies include shift-based inspections, daily/weekly/monthly maintenance, and real-time reporting when faults occur.
What constraints these characteristics impose on the "model access and configuration" workflow
Multi-source heterogeneous data sources require configurations that support cross-format document parsing and field alignment, to avoid format conflicts for equipment information across different systems. Differentiated update frequencies require configurations adapted to dynamically updated datasets, to ensure recalled inspection and maintenance data is up to date. Diverse document types and dedicated fields require configurations that limit recall scope, to avoid irrelevant documents interfering with generated results. The timeliness of statutory inspection records requires configurations that add expired data filtering rules, to prevent generation of maintenance plans and reports that do not meet compliance requirements.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total context of multiple documents such as equipment ledgers, maintenance work orders, and inspection records, covering information needs for typical maintenance scenarios |
quoteMaxToken | 1500 token | Matches the length of a single key record from an equipment inspection sheet, avoiding overly long recalled content that reduces the generation efficiency of exception reports |
temperature | 0.2–0.4 | Ensures the rigor of maintenance plans and reports, reducing irrelevant or subjective generated content |
AIPROXY_API_ENDPOINT | Fill in the privately deployed FastGPT API address | Adapts to local or enterprise intranet calls for equipment management data, preventing leakage of sensitive equipment information |
| Workflow/API tool selection | auto | Supports automatic invocation of retrieval tools to obtain the latest inspection and maintenance data, adapting to dynamically updated equipment status |
maxTokens | 2000–3000 token | Meets the full content length requirements for exception reports and inspection plans, covering complete descriptions of fault analysis and maintenance plans |
Three common errors
- Retrieval results include documents from multiple unrelated equipment, making it impossible to accurately recall inspection data for a specified device. The cause is failure to limit the recall data source scope via configuration, and failure to associate the unique identifier field of the equipment ledger, leading the model to randomly recall information from unrelated devices.
- Calling the FastGPT interface in a locally deployed environment returns a 401 authentication failure. The cause is incorrect configuration of
AIPROXY_API_TOKENand the privately deployed API address, and failure to complete binding settings for call permissions. - Generated maintenance plans include overdue special equipment inspection records. The cause is failure to configure expired data filtering rules, and failure to screen valid data based on the update time of statutory inspection records, leading to generation of content that does not meet compliance requirements.
How to confirm configurations are properly set
- Submit a query that includes a specific equipment number, check whether the retrieval results only include inspection, maintenance, and inspection documents for that device, with no information from unrelated devices.
- Call the FastGPT test interface to generate an exception report, verify that the generation duration of the returned results meets expectations, and there are no timeout errors.
- Check the value of the configured
temperatureparameter, confirm that the generated maintenance plans and report content are rigorous, with no redundant or subjective descriptions. - Import test expired inspection records, verify whether the configuration automatically filters this type of data, and ensure that the generated plans do not include overdue information.
The values provided are common starting points and should be measured against the reader's own samples.