What Data for This Category Looks Like
Data sources for this category include online composition analysis, DCS systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Data update frequencies cover real-time, shift-based, and daily cycles. Daily generated documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. These documents include fields for raw and fuel composition, clinker mineral composition, cement strength indicators, equipment operating parameters, and energy-related metrics. Unlike adjacent categories such as glass, data for this category centers on thermal stability and raw material homogenization requirements for bulk mineral continuous calcination and grinding, so data fields prioritize thermal parameters and raw material mixing ratios.
What Constraints These Characteristics Impose on the "Multi-turn Dialogue and Prompt Engineering" Link
The real-time, shift-based, and daily update frequencies require multi-turn dialogue to dynamically load the latest shift data, and avoid using expired historical context. Multi-source data sources require calling information from different data sources by process segment during dialogue, and prompts must clearly distinguish structured fields across different documents. Multiple document types require specifying mapping rules for corresponding fields in prompts. Multi-turn dialogue must retain historical operation context to support continuous verification of batching adjustments and kiln thermal system adjustments. At the same time, inspection and process record auditing must link error consequence verification logic across all segments to ensure quality indicators meet required standards.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale | |
|---|---|---|---|
chatId | Bind to the unique identifier of the current shift production batch | Associate with inspection and process records for the corresponding batch, avoid cross-batch data confusion | |
messages | Retain the first 20 turns of dialogue context | Cover historical operations for shift batching adjustments and kiln thermal system adjustments | Meet context requirements for multi-turn verification |
variables | Inject structured data from the current shift's DCS and laboratory | Load latest data according to real-time/shift-based update frequency | Ensure inspection data called during dialogue is valid |
| Maximum context length | 8000–12000 token | Calibrated via actual testing | Adapt to the total content length of multiple kiln operation records, laboratory reports, and energy reports |
| File upload configuration | Enable file upload for images, PDF, and Excel formats, with a 1000 MB per-file limit | Adapt to multi-format data import for clinker inspection reports, inspection photos, and energy reports | |
| Model selection scope | Include all large language models accessed via API | Adapt to model capabilities for different inspection scenarios | Meet diverse needs for text extraction and multi-turn dialogue |
This configuration applies to FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: Only single-file type upload is supported in the configuration, making image-based inspection photos and PDF-format laboratory reports unable to be imported. Cause: Upload rules are not configured according to the multi-format document needs of this category, resulting in failure to load some inspection and record data.
- Phenomenon: The model selection dropdown only displays GPT-series models, and GLM-series models cannot be selected. Cause: Permissions for accessing GLM-series models are not synchronized in API configuration, or the multi-model display switch is not enabled.
- Phenomenon: After workflow orchestration, only single-model output is returned, with no step-by-step result processing and no display of intermediate steps disabled. Cause: Multi-node call logic is not configured, or display parameters for intermediate steps are not hidden.
How to Verify Successful Configuration
- Navigate to the application configuration page, check that the
chatIdbinding rule uses the unique identifier of the current shift production batch, and confirm that the session only processes inspection and record data for the corresponding batch. - Initiate a multi-turn dialogue involving raw material batching adjustments, check that the
messagescontext retains parameters and results from historical operations. - Upload a PDF-format laboratory report and Excel-format energy report, confirm that the files can be successfully loaded and parsed into structured data.
- View the model selection dropdown, confirm that all large language models accessed via API are included, and support multi-model calls.
The values provided here are common starting points, and should be validated against the user's own sample data.