What data for this category looks like
Data for this category originates from special equipment safety supervision systems, inspection and testing institution systems, user account books, and operator certificate databases. Updates occur monthly. Commonly involved documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Core fields include equipment number, inspection validity period, maintenance cycle, operator certificate number, and other relevant fields. The total number of associated documents for a single device accumulates gradually over the monthly supervision cycle.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
Monthly updated data requires conversation contexts to only retain valid information from the current cycle. This prevents expired inspection or maintenance data from being included. Differences in the structure of multiple document types require prompt engineering to clearly distinguish field attribution across different documents. This avoids confusion between equipment numbers and operator certificate numbers. High-risk supervision attributes require multi-turn dialogue to accurately bind to the supervision matters of the corresponding device. This ensures responses directly link to the account books and inspection reports of a specific device, and prevents incorrect cross-device associations. The time-consuming nature of manual verification requires multi-turn dialogue to automatically pull associated documents. This reduces the workload of manual retrieval.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to special equipment supervision matter ID | Ensure conversation history in the same scenario does not mix across devices, and matches the unique identifier rules for supervision matters |
messages context length | 10000–12000 characters | Match the total length of monthly associated documents for a single device, and avoid truncating critical information such as inspection cycles and maintenance records |
variables binding | Bind to equipment number, inspection validity period | Link to core fields of the special equipment safety supervision system, and ensure conversation contexts accurately match the current supervised device |
| Temperature parameter | 0.2–0.4 | Ensure the rigor of supervision responses, and avoid generating non-standard statements that do not comply with supervision regulations |
| Maximum context turns | First 8 conversation turns | Match the processing cycle of monthly supervision matters, and avoid interference from redundant historical information in current conversations |
variables update rules | Only modify variable values after user confirmation | Prevent unintended tampering of device information, and meet the operational rigor requirements of supervision scenarios |
Three Common Misconfigurations
- Symptom: The AI responses and user questions in the conversation record list are not correctly associated in interaction order. Cause: The
chatIdis not used to bind the corresponding special equipment supervision matter ID, leading to overlapping conversation history across different devices. - Symptom: After the global variable is first passed in, its preset value cannot be modified in the same conversation. Cause: The real-time update rule for
variablesis not configured, and only the static initial variable value passed in is fixed. - Symptom: The content of each user question within the same conversation cannot be retrieved. Cause: Full historical record pulling for
messagesis not enabled, and only the interface data for a single interaction is called.
How to Verify Correct Configuration
- Initiate a multi-turn inquiry for a single special device, and confirm that the supervision matter ID bound to
chatIdmatches the unique identifier of the current device. - After passing in the initial global variable, submit a modification request in the conversation, and verify that
variablesare correctly updated to the new values. - Check the
messageslist returned by the conversation history interface, and confirm that each user question and AI response correspond one-to-one in interaction order. - Adjust the temperature parameter to the 0.2–0.4 range, initiate a test, and confirm that the response content meets the rigor requirements of the supervision scenario.
The values provided here 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.