What This Category of Data Looks Like
Data sources for this category include EAM/CMMS systems, equipment sensor data, operator inspection records, maintenance work orders, spare parts lists, and special equipment inspection records. Update frequency follows per shift, daily/weekly/monthly, or when a fault occurs. Daily documents include equipment ledgers, inspection forms, maintenance plans, work orders, fault analysis reports, spare parts lists, and special equipment inspection records. Fields covered include equipment number, inspection parameters such as vibration value and temperature, maintenance cycle, spare part model, and inspection validity period. Units include millimeters, degrees Celsius, hours, and other standard units.
Constraints on Multi-turn Dialogue and Prompt Engineering
Multi-source data with inconsistent update frequencies requires multi-turn dialogue to use chatId to bind the current device's unique session identifier. This ensures context is linked to the device's historical data. The large number of highly specialized document fields requires prompts to limit extraction to specific fields such as equipment number and inspection thresholds. This avoids redundant information. Real-time updated sensor and fault data requires real-time data queries during dialogue. Do not rely solely on static knowledge bases. Equipment data is strongly bound to production scheduling. Prompts must clearly state that maintenance windows must comply with workshop scheduling plans. This avoids deviation from business scenarios.
Configuration Specifications
This configuration applies to FastGPT v4.17.0. Specific details are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique session identifier of the current device | Associate historical inspection and maintenance records for the device, ensuring coherent multi-turn dialogue context |
variables | Include fields for equipment number, inspection thresholds, and spare part models | Supplement static business parameters needed for dialogue, narrowing search scope |
messages | Concatenate historical interactions and current queries in session order | Preserve multi-turn dialogue context logic, avoiding repeated questions |
| Timeout setting | 600 seconds | Equipment fault analysis requires retrieving multi-source data, preventing premature request termination |
| Knowledge base collection specification | Bind the inspection knowledge base for the workshop the device belongs to | Focus on core business data, improving retrieval accuracy |
Three Common Configuration Mistakes
- Issue: No settings button appears when selecting a large model with variable reference in the dialogue component. Temperature parameters cannot be adjusted. Root cause: The advanced parameter panel for variable binding was not enabled in system configuration.
- Issue: A single-turn dialogue response takes over 600 seconds, but model call groups show only 1 group. Root cause: The timeout reconnection mechanism for real-time data queries was not configured. Background data pulling blocking was not included in call group statistics.
- Issue: Configuration errors occur after adding the database connection plugin. The dialogue cannot retrieve equipment sensor data. Root cause: Query fields for equipment data were not specified in the plugin, or database connection permissions do not grant access to the equipment ledger table for the corresponding workshop.
How to Verify Proper Configuration
- Initiate a query that includes an equipment number. Confirm that returned historical records link to inspection and maintenance data for that equipment.
- View the session log. Confirm that the session identifier bound by
chatIdmatches the unique identifier of the current device. - Trigger a database query action. Confirm that corresponding spare part inventory or inspection threshold data can be retrieved.
- Adjust the timeout parameter and initiate a long-duration query. Confirm that no premature request termination occurs.
The values provided here are common starting points and should be evaluated against the reader's own samples.