What Data for This Category Looks Like
Data for this category comes from PLM, ERP, MES, supplier materials, validation tests, and customer special requirements. Updates occur irregularly each time a change is initiated. Daily documents include ECN/ECR, change evaluation forms, drawings and BOM version files, process validation reports, transition plans, inventory disposal forms, and customer approval documents. Data fields include change number, version number, effective date, involved equipment model, approval node status, and others. Version numbers use the Vx.x format. Effective dates use the YYYY-MM-DD standard format. Change numbers include a fixed business prefix.
Constraints Imposed by These Characteristics on Workflow Orchestration
Multi-source data access requires workflows to configure multiple nodes that interface with different systems, to ensure accurate data synchronization. Irregular updates require workflows to support dynamic triggering, to adapt to real-time call needs when changes are initiated. Complex document structures require workflows to split processing nodes by document type, to handle ECN approval, BOM verification and other links separately. Rich fields require precise mapping of variables configurations to corresponding business fields, to avoid Q&A errors caused by format mismatches. The node transfer characteristics of change processes require workflows to match node status via nodeId, to control branch execution logic.
How to Set the Configuration
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
variables | Map to corresponding business fields per ECN number, equipment model, effective date | Core category data includes change core identifiers and device association information, to ensure accurate Q&A matching |
nodeId | Configure trigger conditions per the order of change approval nodes | Change processes have clear node transfer logic, to precisely control branch execution timing |
| Timeout threshold | 300-600 seconds | Retrieving data from cross-systems such as PLM and ERP requires reasonable duration, calibrated via actual testing |
| Number of triggered nodes per single run | First 3 core approval nodes | Change processes prioritize initiation, evaluation and verification links, to avoid interference from redundant nodes |
| Global variable assignment | Synchronize from BOM version fields exported from PLM | Core category data sources are PLM, to ensure unified variable sources and compliance with business specifications |
| HTTP request retry count | 2 times | Cross-system requests may experience temporary fluctuations, retries can reduce call failure probability |
Three Common Mistakes
- Phenomenon: A
quote type erroroccurs when calling knowledge base variable references. Cause: The field format referenced byvariablesdoes not match system requirements, for example, wrapping numeric equipment numbers with string quotes or vice versa. - Phenomenon: The tool call module outputs both AI replies and executes the next-level branch at the same time. Cause:
nodeIdtrigger conditions are not correctly configured, and there is no restriction to execute subsequent processes only after the current node is completed. - Phenomenon: HTTP request times out, and requests cannot be added in loop nesting. Cause: The timeout threshold is not adjusted, the default duration is insufficient for cross-system data retrieval, and HTTP request parameters are not correctly configured in loop nodes.
How to Confirm the Configuration Is Complete
- Submit a test change document, check that
variablescorrectly map to corresponding business fields, with no format errors. - Trigger node transfer, confirm that subsequent branches are executed only after the current node is completed, with no parallel execution.
- Modify the HTTP request timeout threshold, verify that the request duration covers cross-system data retrieval needs.
- Check that global variables are synchronized from the specified data source, with fields matching the category data structure.
The values provided are common starting points and should be measured against the reader's own samples.