What this category of data looks like
Basic chemical continuous production process data covers multiple sources and formats. Core documents include operating procedures, process indicator cards, DCS trends, laboratory reports, interlock bypass records, special work permits, and shift logs. Data comes from DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring, energy metering devices, and raw material tank sensors. Data update cycles are divided into three types: real-time (such as DCS trends), shift-based (such as shift logs), and daily (such as laboratory reports). A single process indicator card includes fields such as set values, deviation ranges, and interlock trigger thresholds. Units mostly use industrial standard units such as MPa, ℃, and t/h.
What constraints these characteristics impose on tool calling and plugin workflows
These characteristics impose clear constraints on tool calling and plugin configuration. Multi-source heterogeneous data formats require plugins to adapt to calls for different types including time-series data, documents, and sensor data. Mixed real-time and non-real-time update cycles require distinguishing trigger timing between static document Q&A and real-time data pulling. The business characteristics of strong interlocks and hazardous media require that tool calling results must accurately match process parameters. This avoids false triggers or delays. Differences in fields across multiple document types require tools to correctly identify units and value ranges for corresponding fields during calls.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to mixed calling needs for multiple types of process documents and real-time data |
| Workflow/API function-call configuration | Calibrated based on actual measurements | Matches the complex parameter passing format of basic chemical process parameters |
| Maximum single tool call count | Top 3 entries | Avoids overload, focuses on core process indicators and interlock rules |
| Tool call timeout period | 120–180 seconds | Covers typical time consumption for DCS data pulling and LIMS report queries |
| Knowledge base recall upper limit | Top 8 entries | Matches the core content volume of a single page of process procedures |
| Plugin trigger threshold | Calibrated based on actual measurements | Distinguishes trigger conditions between static document Q&A and real-time process data calls |
Three common errors
- Tool call returns
Human <Instruction>format exception. Phenomenon: Returned content includes unparsed instruction markers. Cause: The parameter passing template forfunctionCallis not correctly configured, and units and field formats of process parameters are not adapted. - Cannot resolve Playwright MCP service, while Amap MCP works normally. Phenomenon: Calls to the Playwright plugin return service resolution failure, while the Amap Maps plugin can be called normally. Cause: The Playwright plugin is not configured with web page rendering adaptation for basic chemical production scenarios, and cannot identify time-series data tags of DCS trend graphs.
- Abnormal tool call result count. Phenomenon: Returned document count exceeds expectations. Cause: A reasonable maximum single tool call count is not set, resulting in too many non-core process auxiliary documents being returned, which interferes with core Q&A.
How to confirm correct configuration
- Initiate a test query involving process indicator verification, and check whether the tool call correctly pulls corresponding DCS trend data.
- View the tool call logs in FastGPT v4.17.0, and confirm that the
toolChoiceparameter has taken effect correctly with no format errors. - Trigger a query related to special work permits, and check whether the plugin can correctly call content from the corresponding document library.
- Simulate an interlock alarm scenario, and verify that the response time of the tool call meets the preset threshold.
The values provided here are common starting points, and should be evaluated against the reader's own samples.