What the data for this category looks like
Core data sources for coal mining production scenarios include underground distributed sensors, ground dispatching systems, employee attendance terminals, and equipment operation and maintenance ledgers. Sensor data is updated in real time. Dispatch logs are generated per shift. Equipment ledgers and employee attendance data are synced daily. The data document structure includes fixed fields: Sensor data contains timestamps, monitoring parameters, and units. Equipment ledgers contain device numbers, models, maintenance cycles, and deployment locations. Attendance data contains employee IDs, shifts, and attendance durations. Field units follow general coal industry specifications: gas concentration uses ppm, temperature uses ℃, and attendance duration uses hours.
What constraints these characteristics impose on tool calling and plugins
The data characteristics of coal mining production impose multiple constraints on the tool calling and plugin link. Real-time monitoring data requires tool calling to support low-latency requests, to meet the time requirements of underground emergency responses. Multi-source data is scattered across sensors, attendance systems, and dispatching systems. Plugins must route calls to corresponding third-party APIs by data type to avoid parameter confusion. When processing shift-generated dispatch logs in batches, limit the total number of requests per call to prevent third-party interface overload. The fixed field structure of equipment ledgers and attendance data requires plugin request parameters to strictly match industry-standard field names. Otherwise, returned content cannot be parsed correctly. The high update frequency of real-time data requires setting reasonable timeout thresholds to avoid request accumulation affecting business processes.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Tool calling timeout duration | 30–60 seconds | Adapts to the request latency requirements of underground sensor real-time data, avoids request accumulation |
| Maximum data volume per request | 500 entries | Matches the batch processing scale of shift dispatch logs, prevents third-party interface overload |
functionCall trigger mode | Triggered per business process node | Aligns with fixed internal office operation processes for coal mining, ensures accurate tool calling timing |
| Response message parsing rules | Match industry-standard fields | Follows coal industry data field naming rules, avoids errors when parsing returned content |
| Plugin call retry times | 2–3 times | Addresses request failures caused by underground network fluctuations, ensures data acquisition stability |
Three Common Misconfigurations
- Phenomenon: The HTTP request module’s return result does not include the Set-Cookie field, so session credentials cannot be obtained. Cause: Response header parsing configuration is not enabled, or extraction rules for the Set-Cookie field are not configured.
- Phenomenon: Local image links returned by API calls cannot be displayed normally in third-party systems, returning a 404 error. Cause: Local relative paths are not replaced with full API access addresses, leading to path parsing errors.
- Phenomenon: API-based conversation calls do not have streaming output, only returning complete results. Cause: Streaming return configuration corresponding to
toolChoiceis not enabled, and streaming parameters are not passed as required by the interface.
How to Confirm Configuration is Effective
In FastGPT v4.17.0, configuration effectiveness can be verified through the following actions:
- Initiate a simulated call request, check if the tool’s returned response message includes expected fields and data formats, and confirm alignment with coal industry standards.
- Simulate batch data requests, observe the call frequency and return status codes of third-party interfaces, confirm no overload error messages appear.
- Trigger preset business process nodes, check if
functionCallandtoolChoicetrigger tool calls as expected, with no delays or false triggers. - Simulate network fluctuation scenarios, check if plugins automatically retry requests according to the configured retry times, and successfully acquire data eventually.
The values provided are common starting points and should be measured against the user’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.