What this category of data looks like
Data originates from EAM/CMMS systems, equipment sensor collection terminals, manual point check records, maintenance work order systems, and statutory inspection archives. Update cycles include shift-by-shift inspections, daily/weekly/monthly maintenance plan updates, and real-time records triggered by equipment faults. Documents contain these fields:
- Equipment ledger: number, model, maintenance cycle
- Point check sheet: check points, status parameters, inspector
- Maintenance plan: execution time, responsible person
- Maintenance work order: fault description, spare parts requisition records
- Special equipment inspection records: inspection date, qualification status
Constraints imposed on multi-turn dialogue and prompt engineering
The diversity of data sources requires multi-turn dialogue to support mixed context calls for structured business data and unstructured manual records. Differences in update frequencies require the dialogue system to prioritize recalling real-time data from fault events, while filtering outdated historical records. The variety of professional fields requires prompt engineering to include clear field definitions and unit standards, preventing large language models from generating outputs that do not meet on-site requirements. The complexity of business associations requires dialogue to bind variables such as spare part inventory and equipment maintenance plans, ensuring outputs align with on-site actual conditions. FastGPT v4.17.0 supports context management configuration for this scenario.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Fixed to the format workshop_equipment group_number | Distinguish point check sessions across different production units to avoid context confusion |
messages | Retain the most recent 20 rounds of dialogue history | Balance context completeness and token consumption |
variables | Bind business fields of the current workshop’s equipment ledger and spare parts list | Allow the large language model to directly call on-site business data, reducing information deviation |
| Prompt template | Include point check sheet field definitions, maintenance cycle requirements, and spare part inventory rules | Align with professional equipment point check scenarios and standardize output formats |
| File expiration days | 7 days, adjustable based on actual testing | Match the update frequency of business data, preventing outdated data from interfering with judgments |
| Multi-turn history truncation | Truncate dialogue history older than 24 hours | Filter outdated point check records and focus on content from the current production cycle |
Three Common Configuration Errors
Each error includes a symptom and root cause:
- Symptom: The large language model returns an empty response. Root cause: The business data source associated with
variablesis not bound, so the large language model cannot access valid equipment point check data. - Symptom: Unclosed reference symbol garbled text appears at the end of dialogue outputs. Root cause: The prompt template does not specify clear closing specifications for output formats, and does not restrict special character usage.
- Symptom: Historical records cannot be read after their expiration date. Root cause: The file expiration parameter was not adjusted, and the default setting was retained, failing to support historical queries for long-cycle maintenance.
How to Confirm Successful Configuration
Complete these verification steps:
- Initiate a point check query dialogue, confirm the large language model can call bound
variablesbusiness fields and return point check requirements for the target equipment. - Test multi-turn dialogue, verify recent dialogue content is retained and irrelevant historical records are excluded.
- Adjust the file expiration days parameter, confirm expired file reading behavior matches expectations.
- Input a fault description, confirm the large language model’s output follows the preset prompt template format, with no garbled text or empty responses.
The values provided are common starting points and should be evaluated against the reader’s own samples.