What This Category's Data Looks Like
This category's data is sourced from daily business documents and system data from cement plant mining, raw material, calcination, grinding, quality control, and energy departments. Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data sources cover online composition analysis systems, DCS control systems, laboratory test data, kiln and mill equipment operation data, raw and fuel ledgers, and energy metering devices. Data update frequencies are divided into three categories: real-time (equipment operation parameters, online composition), shift-by-shift (inspection records, team operation records), and daily (laboratory reports, energy settlement reports). Data fields include raw and fuel batch identifiers, equipment operation parameter values, and test result values. Units include degrees Celsius, revolutions per minute, tons per hour, megapascals, and others.
Constraints on Multi-turn Dialogue and Prompt Engineering
The data characteristics of this category impose clear constraints on the multi-turn dialogue and prompt engineering link. First, data sources are scattered and include both real-time and batch data. Prompts must clearly define call rules for distinguishing between online real-time parameters and historical report data. Second, update frequencies vary widely. Multi-turn dialogue must support filtering corresponding data by real-time, shift-by-shift, and daily time ranges to avoid mixing expired information. Third, daily document types are diverse. Prompts must specify query priorities for different documents, such as prioritizing laboratory reports to verify clinker strength indicators. Finally, core business links such as raw and fuel fluctuations and kiln thermal system adjustments rely on dynamic data. Multi-turn dialogue must support real-time data pulling to ensure returned results match current production status.
Configuration Settings
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
variables parameter configuration | Bind raw and fuel batches, online composition, and equipment operation parameters | Supports dynamic injection of real-time business data, matches the multi-source data characteristics of this category, and is based on the dynamic variable binding feature of FastGPT v4.17.0 |
messages parameter configuration | Concatenate system prompts and historical conversation content by turn | Retains complete quality audit conversation chains, adapts to multi-turn business communication scenarios |
| Maximum context length | 8000–12000 token | Covers all production data required for a single round of quality audits, avoids truncating key indicators |
| Multi-turn dialogue history retention duration | 4 hours | Matches the cycle of shift-by-shift production adjustments, ensures audit process traceability |
| Batch data pull interval | 15 seconds | Matches the real-time update rhythm of online composition analysis, ensures data timeliness |
| Single recall document limit | Top 3 entries | Prioritizes matching the latest laboratory reports and kiln operation records, filters redundant historical data |
Three Common Misconfiguration Issues
- Traces of rule display symbols appear at the end of conversation response results. Cause: The rule display switch was not disabled in prompt configuration, causing the large model to automatically append marked content from system prompts.
- Some requests return empty values when calling workflows concurrently. Cause: The maximum number of workflow runs and maximum input length parameters for batch execution nodes were not adjusted. Concurrent requests exceed the system's default carrying limit.
- Conversations fail to match raw and fuel data for the current production batch. Cause: The current batch identifier was not bound via the
variablesparameter, causing recall of incorrect historical ledger data.
How to Verify Proper Configuration
- Initiate a simulated conversation, input current raw material batching parameters, and check whether the returned results are associated with corresponding business documents.
- Start a multi-turn dialogue process, submit requirements for different links of quality audit in sequence, and check whether the conversation history is fully retained.
- Adjust the number of concurrent requests, initiate multiple sets of test calls, and check whether the returned results have no abnormal marks or empty values.
- Enter the configuration interface, confirm that bound variables, conversation history rules, and recall limit parameters are set as required.
The values provided are common starting points and should be measured against the reader's own samples.