What data for this category looks like
Data for this category originates from product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Daily documents include tooling requirement documents, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Teams update data according to project milestones, product changes, and regular calibrations. Each individual data entry includes fields such as datum parameters, clamping schemes, calibration cycles, and deviation values, with units including millimeters, newtons, hours, and other standard industrial units.
Constraints on multi-turn dialogue and prompt engineering
Diverse heterogeneous document types require teams to distinguish dialogue contexts between technical drawings, metrology data, and acceptance documents, to avoid mixing that causes prompt failures. Following project and change update rhythms, teams must dynamically load latest change histories and calibration records during dialogues, and limit the context window to avoid interference from redundant data. Content containing professional fields such as GD&T and coordinate measuring machine deviations requires prompts to clearly specify field extraction rules, to prevent generation of results that do not meet industry standards. A single dialogue may involve multiple sets of tooling data, so teams must limit the range of project data bound to the current session via dialogue turns.
Configuration Settings
This page’s configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId binding rule | Bind to the unique ID of the tooling project | Ensure the session only associates with the tooling project data currently being audited, isolating cross-project dialogues |
messages context length | Retain 8000–12000 preceding characters | Adapt to the total character count of single-project tooling documents and audit records, aligning with model processing capabilities |
messages history count limit | Retain the first 10 interaction records | Reduce redundant data occupancy, focusing on questions and replies from the current audit phase |
variables scope | Valid within the session | Restrict global variables to take effect only in the current audit session, avoiding data confusion |
Three Common Misconfigurations
- Phenomenon: Shared tooling audit applications show dialogue records from different users to one another. Cause:
chatIdis not correctly bound to the unique identifier of the current user and project, resulting in unisolated session data. - Phenomenon: Calls to the dialogue record list retrieval interface return a fixed number of entries, and pagination queries do not work. Cause: The offset parameter value logic is not correctly configured, and the offset is not adjusted based on the total number of dialogues in the session.
- Phenomenon: After modifying a global variable during an audit dialogue, subsequent interactions cannot read the updated variable value. Cause: The session-valid scope for
variablesis not set, so the variable is overwritten by the global cache and does not update with the dialogue context.
How to Verify Configurations Are Correct
- Share the application with test users, initiate a new audit dialogue, and confirm other test users cannot view the dialogue content, to verify
chatIdbinding works. - Call the dialogue record list retrieval interface, adjust the offset parameter, confirm returned results match the session content after applying the offset, to verify the offset configuration is correct.
- Modify a global variable during the dialogue, trigger the subsequent verification step, confirm the component reads the updated variable value, to check the
variablesscope configuration. - Check the character count of the context window, confirm it does not exceed the preset
messagescontext length limit, to verify the context configuration is appropriate.
The values provided here are common starting points and should be evaluated against the reader’s own samples.