What this category of data looks like
This category of data primarily comes from product drawings and 3D models, CAPP/CAM systems, machine tool collection data, tool management systems, MES systems, and measurement and inspection tool records. Daily documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, inspection sheets, and measuring tool calibration records. Data updates are triggered with each work order, each workpiece machining, sampling inspection link, or tool replacement. Document fields include parameters with clear units such as tool life, spindle speed, and machining dimensional deviation.
What constraints these characteristics impose on the "multi-turn dialogue and prompt words" link
Data updates are triggered by work orders and tool changes. Multi-turn dialogue must bind chatId to associate with the current work order context, to avoid mixing information across work orders. Documents contain multiple types of process parameters with units. Prompt words must explicitly specify calling corresponding fields such as tool life and spindle speed, to ensure that Q&A matches category-specific data. Machine tool collection data has strong real-time performance. Dialogue context must limit the most recent N work order inspection records, to reduce interference from invalid information. Inspection and maintenance items differ across processes. Multi-turn dialogue must confirm parameters iteratively by process, to gradually narrow down the troubleshooting scope.
How to set configurations
These configurations are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current work order ID | Associate with the inspection and maintenance context of a specific work order, avoid mixing information across work orders |
messages | Retain the most recent 8 rounds of dialogue context | Cover the complete troubleshooting process for a single work order, match the process iteration logic of metal cutting machining |
variables | Preset tool life threshold (120 hours), spindle speed threshold (3000 rpm) | Match the category-specific process parameter benchmarks, quickly locate abnormalities |
| Maximum context length | 800–1200 characters | Accommodate inspection records and CNC program fragments for a single process, avoid information overflow |
| Question optimization switch | Enabled | Optimize professional terminology descriptions, more accurately match category-specific documents |
| Single recall upper limit | Top 6 entries | Cover common inspection items and tool parameters for the current process, avoid excessive redundant information |
Three common mistakes
- Phenomenon: LaTeX-formatted process parameter formulas display normally in debug preview, but only display the original LaTeX code after publishing. Cause: Format rendering rules for dialogue content are not configured, and front-end format parsing capability is not enabled in the publishing environment.
- Phenomenon: FastGPT instances deployed via Docker cannot initiate dialogue normally, and remain stuck in the loading state. Cause: Container ports are not correctly mapped or necessary environment variables are not configured, causing the dialogue link to fail to connect.
- Phenomenon: AI dialogue node results inserted in the workflow are appended to the final output content. Cause: The default output switch for this node is not turned off, and the system aggregates all node content into the final result by default.
How to confirm the configuration is complete
- Initiate a simulated work order dialogue, check whether the context is associated with the current work order's specific parameters, verify that the
chatIdbinding takes effect. - Input a fault description containing professional terminology, check whether the reply matches the category-specific process documents and parameters.
- After publishing the application, test whether LaTeX-formatted parameter displays are consistent with the preview, verify the format rendering configuration.
- Trigger the workflow node, check whether intermediate dialogue results are not aggregated into the final output, verify the node output switch configuration.
The values provided are common starting points and should be measured against the reader's own samples.