What this category of data looks like
Data for this category comes from online composition analysis equipment, DCS systems, laboratories, kiln and grinding equipment, raw fuel ledgers, and energy metering devices. Data update frequencies fall into three categories: real-time, shift-based, and daily. Daily documents include raw material batching plans, kiln operation records, test reports, quality control notices, equipment inspection records, and energy reports. Data fields include raw fuel composition ratios, kiln temperature parameters, clinker mineral composition, and cement strength indicators. No fictional statistical values are included.
What constraints this category's characteristics impose on tool calling and plugins
The multi-source, heterogeneous data characteristics of this category require tool calling to adapt to real-time, shift-based, and daily update frequencies. It must support pulling data from multiple channels including online equipment, ledgers, and reports. Diverse document formats require plugins to parse multiple file types: raw material batching plans, kiln operation records, and test reports, among others. Core business workflows rely on multi-dimensional data linkage. Tool calling must associate data such as raw fuel composition, kiln parameters, and quality indicators to prevent decision bias from single data sources. Quality abnormalities require rapid tool responses, so tool calling trigger logic must be bound to key business thresholds.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Auto | Adapt on-demand multi-tool calling, matching audit scenarios triggered by multi-source data |
| Workflow/API function-call configuration | Bind key business thresholds | Correspond to trigger conditions such as raw fuel fluctuations and kiln thermal process abnormalities |
| Tool call timeout threshold | 300 seconds | Match the operation response rhythm of cement production links |
| Maximum number of recalled data entries | Top 10 | Balance information density and decision efficiency, adapting to reference needs for multiple types of reports |
| Plugin execution retry count | 2 times | Address common issues such as temporary equipment disconnection and data pull failures |
| Plugin call frequency limit | 20 times per shift | Match audit frequency in production links, avoiding resource overload |
Three Common Configuration Mistakes
- Tool calling is not triggered after invocation, only natural language responses are returned. This occurs when
toolChoiceis not correctly set to auto mode, or when the business threshold for triggering tool calling is not bound. - Tool calling returns empty results. This happens when the pull format for this category's multi-source data is not adapted, and interfaces for core data sources such as online composition analysis and laboratory reports are not connected.
- Parameter misalignment occurs during sequential tool calls. This is caused by not configuring tool call dependency relationships according to the chronological logic of cement production links: raw material preparation, calcination, and grinding.
How to Verify a Correct Configuration
- Submit a test request that includes raw fuel composition ratio parameters, and confirm that the preset batching adjustment tool call is triggered.
- Review tool call logs to verify that pulled data sources include core data for this category, such as online composition analysis and laboratory reports.
- Simulate a test scenario with abnormal kiln temperature, and confirm that the corresponding thermal process verification tool is triggered.
- Confirm that the deployed FastGPT version is v4.17.0 to ensure that configuration item functions operate as expected.
The values provided here are common starting points, and should be evaluated against the user's own samples.