Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from four core sources: special equipment safety supervision systems, inspection and testing institution systems, user account books, and operator certificate databases. The update frequency is monthly. Commonly involved documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Each document contains fixed fields such as unique equipment number, registration date, inspection cycle, maintenance unit name, operator qualification number, rectification time limit, etc. The character length of a single document ranges from hundreds to thousands of characters, and the data structure varies slightly according to the specific requirements of the supervision jurisdiction.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
Multiple data sources require the dialogue system to clearly define the retrieval scope to avoid confusion of cross-system data. The monthly update frequency means that the dialogue context should not retain historical data older than the current month, otherwise expired inspection cycle or qualification information may be referenced. The differentiated fields of various documents require the prompt to guide users to clearly specify the document type involved in the current dialogue, such as distinguishing between maintenance records and periodic inspection reports. The high-risk supervision attribute requires adding a data verification logic in the dialogue link to ensure that the returned equipment information fully matches the data in the certificate database and supervision system, preventing supervision errors caused by incorrect information.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the exclusive ID of the current supervision task | Ensure that multi-turn dialogue context is tied to a specific supervision task, avoiding cross-task data confusion |
messages | Only retain the last 2 core supervision dialogues | Reduce redundant data and focus on the current hidden danger rectification or qualification verification process |
variables | Inject current supervision jurisdiction and equipment type fields | Limit the retrieval scope to match the exclusive data of local special equipment account books |
| Maximum context window | First 15 dialogues or 7000 characters | Adapt to monthly updated equipment data and avoid expired information interfering with dialogue accuracy |
| Context truncation threshold | Calibrate based on actual testing | Prevent model response delays or content truncation caused by overly long dialogues |
| Workflow node output filtering | Configure specified node visibility as needed | Hide unnecessary intermediate node replies and simplify the dialogue interface for supervision personnel |
Three Common Misconfigurations
- Phenomenon: Replies from multiple <ai dialogue> nodes in the workflow all appear in the chat dialogue associated with the workflow. Cause: The output visibility rules for workflow nodes are not configured, and all node results are included in the dialogue context by default.
- Phenomenon: A connection error is reported when calling the model. Cause: Reasonable interface timeout parameters are not configured, or network access restrictions of the supervision system are not adapted.
- Phenomenon: The equipment information returned by the dialogue does not match the actual account books. Cause: The jurisdiction and equipment type fields are not injected through
variables, causing the retrieval scope to cover data from unrelated regions or equipment categories.
How to Confirm the Configuration is Correct
- Initiate a device qualification verification dialogue, check that the returned content only includes valid data for the specified equipment type within the current jurisdiction.
- Trigger a workflow configured with multiple nodes, check the chat panel to confirm that only the expected AI dialogue node results are visible.
- Simulate a network fluctuation scenario, check whether a connection error is reported, and confirm that the timeout parameter configuration takes effect.
- View the dialogue interface in the non-login scenario, confirm that the custom icon has been correctly loaded.
The configuration values provided above are common starting points, and should be measured against the reader's own samples and operational requirements.
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.