What This Category of Data Looks Like
Data sources for this category include online composition analysis, DCS control systems, laboratory test data, kiln and mill equipment operation logs, raw and fuel ledgers, and energy metering systems. Data update frequency falls into three categories: real-time acquisition of equipment operating parameters, shift-by-shift updates of team patrol and operation records, and daily generation of quality control notices and energy reports. Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment patrol records, and energy metering reports. Document fields include raw material composition parameters, equipment operating parameters, quality inspection indicators, and energy consumption data. Units involved include kilograms, tons, cubic meters, megajoules, and others.
What Constraints Do These Characteristics Impose on the "Multi-turn Dialogue and Prompt Engineering" Link
Data sources for this category are scattered, covering multiple cross-departmental systems. Multi-turn dialogue must integrate structured and unstructured data from different sources. Prompt engineering must clearly specify the data call scope. There are significant differences in data update frequencies. Real-time operating parameters must be prioritized for retrieval. Shift-level and daily documents must be filtered by time intervals. Dialogue context must distinguish data timeliness to ensure content accuracy. Documents contain a large number of professional process fields. Prompt engineering must limit the field boundaries of generated content to avoid generating parameters that do not comply with process specifications. Exception report generation must associate quality and energy consumption data. Multi-turn dialogue must add parameter verification logic to prevent generating results that do not meet process requirements.
How to Configure the Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 characters | Matches the typical length of single documents such as raw material batching plans and kiln operation records, avoids truncating critical process parameters |
| Context filtering rules | Filter by data update time | Aligns with the three update frequencies of real-time, shift-based, and daily data for this category, prioritizes retrieval of the latest DCS and laboratory data |
| Prompt template variables | Bind process field list via variables | Clearly limits the professional fields that must be included in generated reports, avoids missing required items related to quality control and energy consumption |
| Dialogue history call limit | First 5 historical interactions | Filters redundant non-process-related conversations, focuses on the current drafting needs for process documents or exception reports |
| Streaming output toggle | Enabled | Adapts to the need for engineers to view drafting progress in real time, avoids waiting for full results to return |
chatId binding rule | Bind to a single process drafting workflow | Ensures coherent dialogue context for the same workflow, avoids parameter confusion across workflows |
Three Common Configuration Errors
- Symptom: A specific workflow call interrupts mid-process, with no returned content and no error message in the chat window. Cause: The process field list corresponding to
variablesis not correctly bound, causing the prompt to fail to match data sources and triggering context truncation. - Symptom: No streaming output is returned after API calls, only the complete final result. Cause: The streaming output configuration item is not enabled, or the
messageshistory queue is not correctly associated, causing the output logic to not trigger as expected. - Symptom: The annotation function in chat logs cannot be associated with the currently drafted process document. Cause:
chatIdis not bound to the ID of the corresponding process document, causing annotation content to fail to match the current dialogue context.
How to Confirm Successful Configuration
- Initiate a drafting dialogue for a raw material batching plan, check that the returned content includes preset process fields and has no redundant non-process-related parameters.
- Call the API to run a streaming output test, confirm that results are returned segment by segment, with no full results returned all at once.
- View chat logs, confirm that each annotation entry is associated with the process drafting workflow corresponding to the current
chatId. - Replicate the specific workflow call, confirm that the process completes fully and returns results.
The values provided here are common starting points, and should be validated against the reader's own samples.