What this category of data looks like
Data for internal office and employee services in coal mining production mainly comes from production scheduling systems, OA platforms, employee attendance systems, and equipment operation and maintenance logs. Update rhythms vary significantly: equipment operation and maintenance records update in real time with fault handling, employee shift schedules update daily, and safety inspection logs are summarized weekly.
Document structures fall into three categories: Work order documents include fields such as work order ID, equipment number, fault type, and processing duration. Employee documents include fields such as employee ID, position, and shift schedule. Safety documents include fields such as inspection date, hidden danger level, and rectification requirements. Field formats are fixed; for example, equipment numbers use 6-digit numeric codes.
What constraints these characteristics impose on multi-turn dialogue and prompt design
Multi-turn dialogue scenarios in coal mining production often involve continuous work order follow-up, employee shift consultation, and safety process queries. The update rhythms and field format requirements of different data types impose constraints on context management and prompt design for multi-turn dialogue.
Real-time updated equipment operation and maintenance data requires fast retrieval. The context window for multi-turn dialogue should not be too long, to avoid excessive consumption of retrieval resources. Fixed-format fields such as equipment numbers and employee IDs require prompts to adapt to dedicated field recognition logic, to avoid ineffective retrieval.
Spoken employee inquiries such as "The winch on the fully mechanized mining face is broken" require prompts to convert spoken language into standardized retrieval terms. Multi-turn dialogue must retain historical work order IDs and employee position information to ensure response coherence.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | Previous 10 turns of dialogue + latest 3 production work orders | Multi-turn dialogue in coal mining production often covers continuous work order follow-up and shift consultation. 10 turns of history can fully retain the service process, and 3 work orders can associate equipment information related to the current issue |
| Question Optimization | Enabled, calibrated based on actual testing | Employee inquiries often include spoken expressions. Question optimization can convert spoken language into standardized retrieval terms, adapting to coal mining-specific terminology |
| Single Search Limit | Top 6 matching results | Coal mining data entries have clear hierarchy. 6 results can cover common equipment fault and shift query scenarios |
| Response Token Limit | 1500–2500 tokens | Responses for safety rectification plans and work order processing procedures require sufficient length, meeting the information output needs of coal mining business |
variables Variable Binding | Bind employee ID and equipment number fields | Employee inquiries can automatically bring in personal employee IDs to associate shift schedules, and bring in equipment numbers to associate operation and maintenance records, improving response accuracy |
Three common mistakes
- Symptom: An error pops up after entering guide configuration in the debug preview interface. Cause: The custom term library was not entered in the format of coal mining position terminology such as "winch" and "fully mechanized mining face", leading to term library verification failure.
- Symptom: The response token limit is set above 3000, but some responses are only 200 tokens long. Cause: The multi-turn dialogue context occupies most of the token quota, leaving insufficient remaining allowance to support long responses, or the prompt does not clearly distinguish token allocation between context and responses.
- Symptom: The associated knowledge base API call returns no matching results. Cause: Dedicated fields such as coal mining production equipment numbers and work order IDs were not added to the retrieval matching rules, leading to retrieval scope deviating from business requirements.
How to confirm the configuration is correct
- Initiate an inquiry that includes an equipment number, and check whether the response automatically associates the operation and maintenance records of the corresponding equipment.
- Initiate 3 consecutive related inquiries, such as first asking about shift schedules, then asking about equipment status for the corresponding shift, then asking about fault handling procedures, and check whether the
messagesparameter retains historical dialogue context. - Enter a position term from the custom term library, and check whether the question is optimized into a standardized retrieval term.
- View the response logs in FastGPT v4.17.0, and confirm whether the
variablesvariable correctly brings in the current employee's ID information.
The values provided here are common starting points and should be evaluated 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.