What the data for this category looks like
Data for internal office and employee services in rail transit equipment comes primarily from enterprise MES production systems, maintenance management platforms, and human resources attendance systems. Core data fields include equipment number, running kilometers, maintenance cycle, fault code, employee shift schedule, and units such as kilometers, days, and person-times. Real-time operational data is updated in real time. Maintenance ledgers are updated once per quarter. Employee shift data is refreshed daily. Each individual document contains structured content such as device attributes, maintenance records, and service applications. Some associated documents require cross-system field pulling.
Constraints imposed on tool calling and plugins
Rail transit equipment data comes from multiple systems and relies on field associations. This requires pre-configuring multi-data source authorization for tool calling, and using toolChoice to distinguish calling logic for different systems. High-frequency sync of real-time operational data requires tool calling response time to match real-time interface response speed to avoid timeouts. Many structured fields and obvious unit differences require explicitly specifying return fields and units in functionCall to avoid redundant data occupying context. For low-frequency updated data such as maintenance ledgers, configure caching rules to reduce repeated interface requests.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | Assign tools by data source type | Adapt to multi-data source calling requirements across MES production systems, maintenance management platforms, and human resources systems |
functionCall | Explicitly specify return fields and units, for example {"device number":"string","running mileage":"kilometer"} | Match the field and unit characteristics of structured rail transit equipment data to reduce redundant content |
| Tool calling timeout | 30 seconds | Adapt to the interface response speed of real-time operational data to avoid timeout waits |
| Plugin caching cycle | 7 days | Match the update rhythm of low-frequency updated data such as maintenance ledgers to reduce repeated interface requests |
| Context window | 2000 token | Adapt to the length of a single device document to avoid truncation of key information |
| Data visualization plugin | Bind built-in chart generation and PPT generation tools | Adapt to the visual reporting and document output requirements of operational and maintenance data |
Three common mistakes
- Phenomenon: Tool call return results include a large amount of operation logs, occupying context space. Cause: The operation record output switch for tool calling is not turned off, and enabled default logs are passed into the context together.
- Phenomenon: After calling multiple tools, the return result of the last tool overlaps with previous content. Cause:
functionCalldoes not explicitly specify that only the specified fields of the current tool are returned, resulting in residual previous interaction content in the context. - Phenomenon: A single tool call takes longer than the threshold and triggers an error. Cause: The timeout threshold is not adjusted according to the interface response speed of real-time operational data, and general configuration that does not fit the scenario is used.
How to confirm the configuration is complete
- Manually trigger tool calling, check that the return result only includes pre-specified fields and units, with no redundant content.
- View the tool calling time consumption logs, confirm that the time consumption is within the preset timeout threshold range.
- Test cross-data source calling, confirm that different tools can correctly pull data from corresponding systems without permission errors.
- Confirm that the current FastGPT version is v4.17.0 to avoid version compatibility issues.
The values provided are common starting points and should be evaluated 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.