What the data for this category looks like
This category covers full-process data spanning mining, raw material preparation, clinker firing, and grinding. Data sources include online composition analysis equipment, DCS control systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering systems. Data update frequencies fall into three categories: real-time (such as kiln thermal parameters), shift-based (such as equipment inspection records), and daily (such as laboratory reports and quality control notices). Daily documents include raw material batching plans, kiln operation logs, clinker strength laboratory reports, equipment inspection ledgers, and energy consumption reports. Fields covered include material composition, equipment operating parameters, metered values, and time stamps.
What constraints these characteristics impose on tool calling and plugin workflows
The multi-source heterogeneous data and differentiated update frequencies of this category require tool calling to adapt to mixed real-time and batch data pulling logic. Differences in the structure of different document types require plugins to support targeted field extraction rules. For example, extracting clinker strength values from laboratory reports, or capturing kiln temperature fluctuation data from DCS logs. The real-time requirements of industrial equipment data mean the tool calling timeout threshold must match the response window of process nodes. It must also support batch parsing for ledger documents, to avoid performance degradation from processing too much historical data in a single call.
How to Configure the Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to automatic scheduling of multi-source data, matching tool calling requirements across multiple links in the cement production process |
| Workflow/API function-call configuration | enabled | Enables explicit function calling rules to ensure structured accuracy in industrial parameter extraction |
| Tool calling timeout threshold | 300 seconds | Matches the real-time response window of kiln thermal data to avoid blocking production processes |
| Batch document parsing limit | Top 20 entries | Adapts to the processing scale of shift-based inspection records and daily ledgers, controlling single-call load |
| Context window size | 8000–12000 token | Adapts to parsing requirements for long-text batching plans and kiln operation logs |
| Custom tool parameter range | Calibrated based on actual measurements | Batching plans and metered parameters vary across enterprises, so adjustments must be made based on actual business needs |
Three Common Configuration Errors
- Symptom: Connection timeout error when calling a local large model in a Linux deployment environment. Cause: The network access whitelist for the local model is not configured, or the corresponding port is not mapped for the FastGPT container.
- Symptom: Response time for calling the file parsing tool exceeds expectations. Cause: A single call parses more documents than the batch processing limit, exceeding the tool's load capacity.
- Symptom: The file parsing tool cannot be triggered when calling the DeepSeek-R1 series large models. Cause: The explicit calling rule for the corresponding tool is not enabled in the
functionCallconfiguration, causing the model to fail to match the calling logic.
How to Verify Proper Configuration
- Execute a test call, enter queries related to kiln temperature or clinker strength, and confirm whether the results automatically pulled by the tool from the corresponding data sources meet expectations.
- Upload a single laboratory report or kiln operation log, and verify whether the tool can correctly extract the values and identifiers of preset fields.
- Check the tool calling logs to confirm that frequent timeout errors are not triggered by the timeout threshold.
- Adjust custom tool parameters, and verify whether optional configuration items are displayed normally in the conversation interface.
The values provided here are common starting points and should be measured against the reader's own samples.