What the data for this category looks like
The data for this category mainly comes from terminal management platforms, asset systems, user trouble reporting channels, and software repositories. Daily produced documents include terminal trouble tickets, installation lists, software installation records, and remote assistance records. Fields include terminal serial number, fault phenomenon description, installation time, software version number, remote assistance duration, and more. Data updates occur daily or when each request is triggered.
What constraints do these characteristics impose on the multi-turn dialogue and prompt words link
The data sources for this category are scattered, covering multiple independent systems such as terminal management, assets, and trouble reporting. Therefore, messages for multi-turn dialogue must be spliced by request source to avoid cross-system information confusion. Daily document fields are numerous and have inconsistent structures, so prompt words must clearly specify the extraction priority of core fields including fault type and terminal serial number. Data updates are triggered by requests, so dialogue context must support dynamic loading of the latest terminal asset and trouble reporting data. A large amount of unstructured fault description content exists, so prompt words must guide the model to organize output according to a fixed document format.
How to set the configurations
The following configurations are based on the functional logic of FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the terminal's unique serial number | Ensure each terminal's dialogue context is independent, avoiding cross-device information confusion |
messages | Retain the last 10 dialogue turns + the latest 3 asset data entries for the current terminal | Balance context completeness and response speed, adapting to short dialogue requirements in terminal scenarios |
| Maximum context length | 8000–12000 characters | Cover the conventional content length of terminal fault troubleshooting and installation record organization, calibrated via actual testing |
variables | Inject the current terminal's asset ID and trouble reporting timestamp | Allow the model to obtain real-time latest terminal data, adapting to data sources updated per request |
| Prompt template | Fixed to include instructions of "prioritize extracting terminal serial number" and "sort fault steps by time" | Adapt to the document structure and core business requirements of this category |
| Dialogue timeout duration | 60 seconds | Match daily response timelines for desktop terminal support, avoiding long waits that disrupt engineer work |
Three common mistakes
- Streamed output does not segment as expected, leading to out-of-order content on the frontend page. The cause is failure to correctly bind
chatId, resulting in context mixing and dialogue flows not aligned by terminal unique identifiers. - AI dialogue output in workflows is directly displayed on the page, unable to be processed by subsequent components. The cause is failure to disable the automatic output switch and configure
variablesto specify an output callback node. - The dialogue log annotation function cannot associate corresponding terminal documents. The cause is failure to bind annotation fields to terminal serial numbers, resulting in annotation information being disconnected from specific devices.
How to confirm the configuration is correct
- Initiate a test request for terminal fault troubleshooting, check whether the returned result includes the current terminal's serial number and latest asset information.
- View the dialogue log, confirm that the
chatIdof each dialogue matches the unique identifier of the corresponding terminal. - Trigger the AI dialogue node in the workflow, confirm that output content must be processed by the specified component before being displayed on the page.
- Test the timeout scenario, confirm that the request returns within 60 seconds or triggers a timeout prompt.
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 2026-10-19.