Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data sources for construction machinery internal office and employee services mainly come from equipment operation and maintenance systems, enterprise ERP, employee attendance systems, and internal document libraries. Equipment operation and maintenance logs include fields such as equipment ID, running duration (unit: hours), fault code, and timestamp. They update every hour or in real time. Attendance data includes employee ID, department, and clock-in time, and updates daily. Maintenance records include fields such as maintenance personnel, equipment number, replaced parts, and time spent (unit: minutes). They update once after single entry. Internal documents are mostly equipment operation manuals and maintenance specifications, with a chapter-based structure and a large number of industrial technical terms.
What constraints these characteristics impose on model access and configuration
The real-time nature of equipment operation and maintenance logs requires model calls to match data update rhythms. This avoids excessive latency that affects internal office response speeds. Field differences in multi-source heterogeneous data require field standardization configuration before access. This ensures the model can uniformly recognize information from different data sources. The volume of long documents and density of technical terms require adjusting context window parameters. This ensures core business content is not truncated, while adapting to industrial semantic understanding needs. Differences in data source update frequencies across scenarios require configuring dynamic call rules. This adapts to different processing logic for real-time operation and maintenance data and static documents.
How to set configurations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context splicing needs for multi-source long texts such as equipment operation and maintenance logs and maintenance manuals, avoiding truncation of core business data |
temperature | 0.2–0.4 | Used for internal office Q&A scenarios, reduces random output, and ensures accuracy of answers for equipment fault troubleshooting and attendance inquiries |
AIPROXY_API_ENDPOINT | Intranet-deployed FastGPT node address | Adapts to security requirements of Chinese state-owned enterprise and public sector intranets, preventing business data exfiltration |
AIPROXY_API_TOKEN | Token bound to specified model permissions | Restricts only authorized models to be called, complying with internal data access control requirements |
maxTokens | 1000–1500 token | Controls single-round response output length, adapts to concise and fast internal office consultation scenarios, avoiding invalid long text outputs |
toolChoice | auto | Allows the model to independently choose whether to call tools, adapting to tool call needs such as equipment operation and maintenance work order creation and attendance query |
Three common mistakes
- A
403 Forbiddenerror occurs, prompting that the token does not have permission to use the model. The cause is that the configuredAIPROXY_API_TOKENis not bound to the model permissions required for the corresponding construction machinery scenario, or the token scope does not cover the domain name used for internal office calls. - The model return result does not match equipment operation and maintenance data. The cause is that the value of
maxContextis too small, truncating core context information such as equipment historical fault records. - Single-round response time exceeds expectations. The cause is that the value of
maxTokensis too large, exceeding the upper limit of model inference resources deployed on the intranet.
How to confirm the configuration is complete
- Call the test interface, pass real business fields such as equipment ID and fault code, and check if the model return result includes corresponding business logic.
- View the token permission configuration page, confirm that
AIPROXY_API_TOKENis bound to all models required for the current scenario. - Upload a copy of the equipment operation manual, trigger the context splicing process, and check that the return result does not truncate critical content.
- View the intranet monitoring panel, confirm that model call latency meets internal office real-time requirements.
The values provided here 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.