Data Characteristics for This Category
Process data sources for pulp and papermaking include wood pulp and waste paper batches, DCS/QCS systems, on-machine measuring equipment for paper machines, laboratories, energy and water treatment systems. Data update rhythms fall into three categories: real-time, per shift, and per roll, corresponding to recording frequencies for different process links. Daily documents include stock preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory test reports, and sheet break and product change ledgers. Each document contains fields such as batch number, operation time, and corresponding process parameters. Parameter fields include beating degree (unit °SR), grammage (unit g/㎡), thickness (unit mm) and other dedicated units.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data sources are scattered across multiple systems. Multi-turn dialogue must gradually integrate associated parameters from different systems. Prompts must clearly specify identification rules for data from each system. Update intervals vary widely. Real-time data must be prioritized into the current dialogue context. Expired per-shift or per-roll data must be filtered in a timely manner. Document types are diverse with different field formats. Prompts must distinguish identification logic for different documents to avoid parameter confusion. Cross-directional quality parameters for web production must be tracked separately. Multi-turn dialogue needs to guide supplementary collection of process data for corresponding dimensions.
Configuration Guidelines
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum context length | 800–1200 characters | Adapts to the single-paragraph parameter length of pulp and papermaking process documents, avoids context overflow |
| Number of retained conversation history entries per interaction | First 6 entries | Matches the update frequency of per-shift process records, filters expired historical conversation information |
variables binding scope | Bind process parameters for the current production batch | Ensures variables called during multi-turn dialogue match the current production batch |
| Conversation record cleanup cycle | Once per shift | Matches the production team shift change rhythm, avoids redundant data occupying storage space |
messages format validation rules | Validate by document type | Ensures input dialogue content complies with field specifications for corresponding process documents |
chatId isolation rules | Isolate by production workshop | Prevents confusion of dialogue contexts across workshops, ensures data accuracy |
Three Common Configuration Errors
- Phenomenon: After executing a conversation record deletion operation, associated process logs disappear simultaneously. Cause: Independent storage rules for conversation records and logs are not configured, and the two are mistakenly bound as the same data entry.
- Phenomenon: Missing fields occur when referencing multi-turn dialogue parameters in prompts. Cause: The names of required process document fields are not explicitly specified in the prompt, leading to incomplete context extraction.
- Phenomenon: An empty result is returned when calling a command to obtain prompt content for a specified application under a specified account. Cause:
chatIdis not correctly bound to the permission scope of the target application, leading to failed permission verification.
How to Verify Correct Configuration
- Initiate a test dialogue containing stock preparation sheet parameters, check whether the returned content correctly associates the bound variables for the current production batch.
- Execute a single conversation record deletion operation, confirm that the corresponding process log is not deleted simultaneously.
- Call the specified command to obtain the prompt content of the target application, check whether the returned result matches the preset configuration content.
- Initiate a test dialogue across production workshops, confirm that the context does not mix process parameter data from other workshops.
Note: The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.