Data for This Category
Data for this category comes from PLM, ERP, MES, supplier materials, validation tests, and customer-specific requirements. Updates are triggered irregularly with each process or engineering change. Daily documents include ECN/ECR forms, change assessment reports, drawings and BOM version records, process validation reports, production switching plans, inventory disposal documents, and customer approval files. Data fields include change number, version identifier, effective node, affected production process scope, associated material code, validation results, and more. There is no fixed batch update cycle.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Data sources for this category are scattered and heterogeneous. Multi-turn dialogue must first pull cross-system context to ensure called change information uses the latest version. Data updates have no fixed cycle. Dialogue context cache duration must match change trigger rhythms to avoid loading expired ECN/ECR or process validation records. Document types cover forms, drawings, reports, and more. Prompt engineering must preset retrieval priority rules for different document types. Associated fields include production processes and material codes. Multi-turn dialogue must break down problems step by step along the change chain to prevent context overflow caused by passing excessive associated data at one time.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 token | Adapts to the total character count of multiple documents such as ECN and BOM, avoids context overflow |
messages History Filter | Retain change conversations from the last 30 days | Matches the irregular update rhythm of data in this category, filters expired context |
variables Binding | Bind change number and version number | Quickly locate all associated documents for a corresponding change |
| Single Dialogue Turn Limit | First 8 turns | Matches the typical follow-up chain for change analysis, prevents meaningless loops |
| Streamed Return Interval | Calibrated via actual testing (1–4 seconds) | Adapts to front-end display requirements, matches response speed requirements for different scenarios |
| Session Isolation | Store independently by chatId | Prevents context interference between different change sessions |
Three Common Misconfigurations
- Issue: After calling the dialogue API, the content displayed on the conversation details page does not match the actual returned response. After multiple calls, the details still show fixed old content. Cause: The
chatIdparameter is not correctly bound. Each call reuses the initial session context without updating the session state. - Issue: The streamed output returns at a fixed interval of approximately 4 seconds, and cannot be adjusted to 1 or 2 seconds. Cause: The interval control rule for streamed returns is not enabled in the dialogue configuration. The platform's default fixed delay parameter is used instead.
- Issue: Text confusion and logical disconnection occur when merging output results from two AI conversations. Cause: Independent context isolation is not configured for each dialogue session. Context data from the two sessions is mixed into the output content.
Verify Proper Configuration
- Launch a multi-turn dialogue for change traceability. Check if the
messagesparameter includes the latest ECN number and version information. Confirm that no expired data is loaded in the context. - Call the streamed output interface. Observe the interval between returned data. Confirm that it matches the preset 1–4 second control rule.
- After binding the
variablesparameter, launch a dialogue. Check if associated documents for the corresponding change can be located quickly, with no omissions or mismatches. - Test two independent change sessions. Confirm that the
chatIdparameter for each session is unique. Dialogue contexts do not interfere with each other.
The values provided are common starting points and should be measured against the reader's own samples.