What the data for this category looks like
Mineral mining and beneficiation data mainly comes from underground real-time sensors, DCS control systems for mineral processing flows, production scheduling ledgers, and employee attendance systems. Real-time sensor data updates every second. Scheduling ledgers are archived daily. Monthly production reports are generated each month. Single data entries include a timestamp, point identifier, corresponding value and unit. Mineral processing process data includes process name, material throughput, and energy consumption parameters. Employee data includes employee ID, shift, attendance duration, and job information.
What constraints these characteristics impose on tool calling and plugin workflows
The second-level updates of real-time sensor data require tool calling responses to complete within 5 seconds. Otherwise, on-site scheduling decisions cannot be supported. Multiple heterogeneous data sources require plugins to adapt to different interface protocols. Underground sensors use HTTP push interfaces. Production ledgers use relational database query interfaces. Employee data involves job and attendance privacy. Plugins must include built-in permission verification logic, only allowing authorized roles to initiate calls. Numerical units vary across different processes. Plugins must pre-set unit conversion rules to avoid parameter confusion.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapts to multi-tool calling scenarios for mineral mining and beneficiation, automatically selecting matching tools |
| Tool call timeout period | 3–5 seconds | Matches the update rhythm of underground real-time sensor data, avoiding data expiration |
| Single tool call return count | Top 3 entries | Limits per-round call data volume, adapting to on-site production rapid response needs |
| Permission verification switch | Enabled | Meets privacy compliance requirements for employee data and production ledgers |
functionCall | Enabled | Forces the model to call tools in the specified format, avoiding output format errors |
| Plugin cache duration | 10 minutes | Balances real-time performance and interface call frequency, adapting to daily updated scheduling ledger data |
Three Common Misconfigurations
- The model uses inference mode exclusively, without calling configured tools. Cause: The
toolChoiceparameter is not set correctly. Some models enable inference mode by default, bypassing the tool calling workflow. - Tool calling returns a connection timeout prompt. Cause: Correct data source interfaces are not configured, or the timeout period is set too short to adapt to the acquisition rhythm of real-time sensor data. FastGPT v4.17.0 or later must be used to configure related plugins.
- Numerical units returned by tools are inconsistent. Cause: Unit conversion rules are not pre-set in plugins, and numerical units across different processes are not unified, causing the model to fail to parse parameters correctly.
How to Confirm Configurations Are Complete
- Initiate a simulated call, and verify that the data source data returned by the tool matches the latest data from the actual production system.
- Attempt to call tools using an unauthorized account, and confirm that prompt information related to permission verification is returned.
- Trigger a tool call, and verify that the model output strictly follows the specified format for tool calling, with no irrelevant natural language content generated.
- Confirm that the current FastGPT version is v4.17.0 or later, ensuring that the tool calling plugin function is properly enabled.
The values provided here 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.