What this category of data looks like
Coal chemical industry ledger data originates from the DCS control system of production workshops, shift laboratory reports, raw material procurement and product outbound ledgers. Workshop operation data updates every hour. Laboratory reports update daily. Procurement and outbound ledgers update daily. Documents fall into three categories: workshop-level real-time ledgers, monthly summary ledgers, and annual analysis reports. Fields include workshop name, equipment number, operation duration, temperature, pressure, raw material consumption, product output, ash content, calorific value, and more. Units include hour, degrees Celsius, megapascals, tons, and kilograms.
Constraints imposed by these data characteristics on multi-turn dialogue and prompt engineering
Reference only; verify against the target FastGPT release and deployment documentation.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Single conversation context limit | 8000–12000 characters | Coal chemical industry single workshop ledger data volume is large, this range accommodates multi-turn interactions and complete data sources |
| Dialogue timeout period | 300–600 seconds | Coal chemical industry ledger summary requires processing multi-dimensional data, sufficient response time must be reserved |
| Maximum retained historical dialogue count | First 20 entries | Multi-turn ledger summary requires retaining key parameter adjustment records, excessive historical content will occupy context space |
| System prompt binding variables | Only bind fields exclusive to coal chemical industry ledgers | Prevent the model from introducing irrelevant parameters outside the coal chemical industry domain, ensuring output complies with business specifications |
| Dialogue trigger rule | Trigger based on user-specified workshop and time range | Aligns with the coal chemical industry ledger summary business logic based on workshop and cycle |
Three Common Mistakes
- Symptom: Dialogue records show as empty and display as a new conversation after refreshing the dialogue page. Cause: The
chatIdparameter is not correctly bound. A new conversation identifier is generated for each session, preventing historical records from being associated. - Symptom: The AI prompt indicates no files uploaded, but coal chemical industry ledger documents have already been added to the system prompt. Cause: The system prompt does not bind the document path or content via the
variablesparameter, so the model cannot recognize the preset data source range. - Symptom: The generated report has no Word download link, and the finished product cannot be exported. Cause: No export node is configured in the workflow, or the generated report content is not correctly passed to the export link.
How to Confirm Correct Configuration
- Initiate a summary request for a single workshop ledger, and verify that the dialogue history is correctly retained in the context window.
- Upload coal chemical industry ledger documents, trigger a dialogue, and confirm that the model can correctly recognize and use the professional fields in the documents.
- Test refreshing the dialogue page, and confirm that historical dialogue records are not lost and the conversation identifier remains consistent.
- Trigger the report generation process, and confirm that the workflow includes an export link and generates an accessible download link.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.