This page is developed based on the configuration rules for FastGPT v4.17.0.
What the data for this category looks like
Core data for this category comes from daily operation and maintenance documents of manufacturing enterprise equipment departments, workshop maintenance teams, and public utility departments. These include equipment ledgers, point inspection forms, maintenance plans, work orders, fault analysis, spare parts lists, and special equipment inspection records. Data sources cover EAM/CMMS systems, equipment sensor collection terminals, operator point inspection records, historical maintenance archives, spare parts inventory ledgers, and statutory inspection reports. Data update frequencies include shift-by-shift inspections, daily/weekly/monthly maintenance plan updates, and real-time records when faults occur. Document fields include equipment number, point inspection time, operating parameters, maintenance personnel, spare parts code, inspection validity period, and others. There is no unified standardized format, and some fields require cross-system association and extraction.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources and differentiated update rhythms require model access configuration to adapt to different data access permissions and update delays. Real-time requirements for equipment sensor data require configuration to support low-latency context calls. Compliance requirements for statutory inspection records require configuration to lock the reference scope of specific documents. Non-standardized fields across multiple document types require configuration to support accurate field extraction and association, avoiding confusion between similar fields such as equipment number and spare parts code. Cross-system data access requirements require configuration to adapt to access rules of internal proxy services, ensuring data security.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers complete point inspection, maintenance, and upkeep records from a single team shift change, ensuring completeness of multi-document context |
maxTokens | 2000–3000 token | Meets detailed response requirements for fault analysis and spare part replacement recommendations, and adapts to output limits of most large model APIs |
temperature | 0.1–0.3 | Maintains rigor in point inspection record audits, reduces irrelevant divergent content, and complies with compliance requirements for equipment operation and maintenance |
quoteMaxToken | 1500–2000 token | Ensures recalled core document content such as point inspection forms and work orders is not truncated, protecting the integrity of audit basis |
AIPROXY_API_ENDPOINT | Internal enterprise-deployed proxy service address | Adapts to data access for internal systems such as EAM/CMMS, avoiding permission restrictions from cross-network access |
AIPROXY_API_TOKEN | Access key bound to the proxy service | Ensures access security for internal equipment operation and maintenance data, preventing unauthorized access |
Three common configuration mistakes
- Phenomenon: Audit results returned by the model do not reference specific operating parameter values in point inspection forms. Cause: The
quoteMaxTokenparameter is not configured correctly, leading to truncated recalled context and failure to extract core field information from documents. - Phenomenon: Multi-turn conversations cannot continue analysis based on previous equipment fault questions. Cause: The
maxContextparameter is not adjusted to cover multi-turn interaction history, leading to context loss and inability to associate earlier fault scenarios from questions. - Phenomenon: Permission errors are returned when calling the model. Cause: The
AIPROXY_API_TOKENis not configured correctly, or the proxy service address does not open access permissions for internal EAM systems, leading to blocked data access.
How to confirm configuration is complete
- Upload a single complete equipment point inspection form and corresponding work order, trigger model audit, and verify if returned results extract core field information from the document.
- Initiate two consecutive equipment fault-related questions, verify if the second reply associates the fault scenario raised in the first question.
- Call the configured proxy interface, verify if the interface return status and permission verification results meet expectations.
- Adjust the model temperature parameter, verify if generated audit opinions maintain a rigorous expression style without subjective divergent content.
The values provided here are common starting points and should be evaluated against the reader's own samples.