What the Data for This Category Looks Like
The daily data sources for basic chemical continuous production include DCS/SIS systems, online analyzers, LIMS laboratory management systems, equipment condition monitoring modules, energy metering systems, and raw material tank monitoring modules. Data update frequencies fall into three categories: real-time, shift-based, and daily. Each frequency corresponds to different types of business documents. Typical documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift daily reports. Fields and units include process temperature (℃), system pressure (MPa), material flow rate (m³/h), material concentration (%), and interlock thresholds (fixed range), among others.
Constraints for Tool Calling and Plugins
Data sources for this category are scattered, with large differences in update frequencies. Tool calling plugins must adapt to the API formats of multiple data sources to prevent verification failures caused by incompatible data formats. Strong interlock control business requirements demand strict permission and result verification logic for tool calls. This prevents misoperations that could cause device abnormalities. Multiple document types require plugins to recognize unique fields for each document type. For example, interlock bypass records must match interlock ID and effective time fields to ensure accurate audit data. Real-time data updates at high frequencies, so tool call response delays must stay within a reasonable range to maintain audit timeliness. Enabling functionCall supports complex process data verification logic.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multiple tool calling requirements, automatically matches plugins for inspection and audit |
| Workflow/API function-call configuration | enabled | Enables custom function calling to support complex process data verification logic |
| Maximum single tool call count | First 3 times | Prevents excessive resource usage from circular calls, aligns with steady-state control logic for chemical processes |
| Tool call timeout | 60 seconds | Meets real-time process data response requirements, avoids delaying audit workflows from excessive waiting |
| Knowledge base reference variable | Workflow input parameter | Enables dynamic switching of process-specific knowledge bases, supports audit needs for different production units |
| Tool call result verification | Enabled | Validates field completeness and unit consistency of returned data, complies with chemical data specification requirements |
Three Common Misconfiguration Issues
- Phenomenon: An error message returns when calling an external API, and process data cannot be retrieved. Cause: No API configuration for the target data source is added to the plugin list, so the call target cannot be identified.
- Phenomenon: Tool calls run repeatedly, with logs showing multiple triggers of the same plugin node. Cause: No tool call termination node is configured at the workflow end, causing cyclic call triggers.
- Phenomenon: The knowledge base search node fails to load passed variable values, resulting in empty search results. Cause: Workflow input parameters are not correctly bound to the knowledge base selection field, so variables are not passed to the node configuration.
How to Verify Proper Configuration
- Trigger a test workflow, check the tool call logs in FastGPT v4.17.0, and confirm that called plugins match configured items.
- Submit test variables, check if the knowledge base search node loads the process-specific knowledge base, and confirm variable transfer works.
- Simulate a tool call timeout scenario, confirm that preset timeout handling logic triggers, and verify the timeout configuration is active.
- Run the test process more than 3 times consecutively, confirm no repeated execution occurs, and verify the termination configuration is active.
The values provided here are common starting points and should be measured against the reader’s own samples.