What the data for this category looks like
The internal office and employee service data for power generation equipment manufacturing primarily comes from equipment operation and maintenance systems, production work order platforms, personnel management modules, and spare parts inventory systems. Operation and maintenance logs update in real time as equipment runs. Work orders update based on submission and processing timelines. Personnel data syncs daily or monthly. Document structures include fields such as equipment ID, operating speed (revolutions per minute), fault code, work order number, affiliated workshop, employee ID, attendance duration (hours), spare parts in-and-out quantity (pieces), and some documents include structured fault troubleshooting steps and parameter records.
What Constraints These Characteristics Impose on Tool Calling and Plugins
Multi-source heterogeneous data characteristics of power generation equipment manufacturing create multiple constraints for tool calling and plugin configuration. Structured equipment operating parameters include numerical fields with attached units. This requires tool calling to support parameter format verification and unit conversion, to prevent call failures caused by mismatched values and units. Data from multiple systems has inconsistent field names. Plugins must support custom field mapping rules to adapt to the field naming conventions of different systems. Real-time updated operation and maintenance logs and dynamically changing spare parts inventory require tool calling interface timeout periods to align with real-time data pull needs. Plugins must also support adjusting data pull scope based on business scenarios. Employee service data involves permission isolation, so plugins must configure role-level data access restrictions.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | Automatically select based on scenario | Adapts to tool calling requirements across multiple business scenarios for power generation equipment manufacturing. When a specific tool needs to be called consistently, this can be changed to specify the tool name |
| Tool calling timeout period | 30–60 seconds | Adapts to the pull rhythm of real-time operation and maintenance data, avoiding process interruptions caused by excessive waiting time |
| Field mapping rules | Custom mapping per system | Resolves field name differences in multi-source heterogeneous data, adapting to field formats of different systems such as operation and maintenance, work orders, and personnel |
| Parameter verification switch | Enabled | Verifies the matching of values and units for equipment operating parameters, reducing call failures |
functionCall | Enable structured output mode | Ensures results returned by tool calling conform to preset formats, facilitating subsequent unified logical processing |
| Custom channel configuration | Calibrated based on actual testing | Adapts to access requirements for third-party models or tools, such as model access deployed based on Ollama |
Three Common Configuration Mistakes
- Symptom: Tool calling returns empty results or fails to trigger the specified tool. Logs show the model did not generate properly formatted function call instructions. Cause: The
functionCallstructured output mode is not enabled, so the model cannot correctly recognize the tool calling format. - Symptom: Tool calling fails when using a third-party deployed model, such as Qwen2.5 deployed via Ollama. An interface error is returned. Cause: The custom channel configuration does not correctly match the model's tool calling interface address and parameter format, so the request cannot be parsed properly.
- Symptom: Tool calls for multi-source data return missing fields or incorrect formats. Cause: No custom field mapping rules are configured, and the field naming differences across systems are not addressed, so the tool cannot correctly pull target data.
How to Verify the Configuration Is Complete
- Submit a test query with a clear tool calling requirement, and check if the interface triggers the corresponding tool calling process.
- Review tool calling logs to confirm the returned parameter format matches the preset structured output requirements.
- Test tool calling results across multiple business scenarios to confirm data fields align with actual business needs.
- Test the model accessed via the custom channel to confirm tool calling requests send successfully and return valid results.
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.