What the Data for This Category Looks Like
Business data for electric motor and generator manufacturing comes from electromagnetic design systems, copper wire and insulating material batch ledgers, winding equipment, balancing machines, electrical test benches, and ERP/MES systems. Daily generated documents include winding process cards, insulation treatment records, dynamic balance reports, dielectric strength test reports, and type and factory test reports. Data is updated per individual motor or product batch. All associated documents are synchronized whenever a product type change occurs. Document fields include professional parameters such as winding turns, paint dipping process parameters, vibration values, temperature rise data, with units including turns, degrees Celsius, newton meters, and others.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Category data updates per individual unit or product batch. Workflows must bind to the unique identifier of the corresponding batch to avoid cross-batch data confusion.
Multi-source heterogeneous data sources require workflows to configure multiple nodes to connect to different systems and equipment, and unify data formats.
Specialized fields in professional documents require classification nodes to set branches for different document types such as winding processes and test reports. This ensures that subsequent called knowledge bases and AI conversations match the corresponding business scenarios.
Strict data association requirements mandate that workflows include a parameter verification step to prevent incorrect data from entering the quotation and document preparation stage.
How to Set the Configuration
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Question Classification Node | Match branches by document type | Category documents are divided into types such as winding processes, test reports, etc., and different business scenarios require shunt processing |
variables | Bind the batch number and electromagnetic design parameters of a single motor | Category data is updated per batch, and exclusive business parameters must be passed in the workflow |
| Node Jump Configuration | Specify the target branch by nodeId | Supports skipping the initial classification node and directly entering the corresponding business branch |
| Knowledge Base Association | Associate winding process library and test report library | Core business documents for this category are process and test related materials |
| Context Window Size | 8000–12000 token | Professional documents for this category have relatively long content, requiring complete loading of parameters for AI generation |
| Failure Retry Count | 2–3 times | Temporary interruptions may occur when pulling device data, and a fault tolerance mechanism must be set |
Three Common Mistakes
- Text classification and extraction nodes return null values, and the interface displays no matching results. The cause is that extraction rules are not configured for category-specific fields such as winding turns and dielectric strength values, only general classification logic is used, and the category-specific document library is not associated.
- The workflow cannot exit the classification branch after completing the AI conversation, and the log shows node jump exceptions. The cause is that the
nodeIdparameter is not correctly set to bind the target branch node, and the initial classification node is mistakenly used as a loop trigger point, causing the process to get stuck in the classification stage. - Importing old workflows fails after upgrading the open source version, and the interface prompts incompatible configuration. The cause is that the workflow configuration file adapted to the new version is not exported, and the old version application package is directly copied, resulting in parameter formats that do not match FastGPT v4.17.0.
How to Confirm the Configuration Is Properly Configured
- Trigger the test workflow, upload category-specific documents, and check whether the classification node correctly shunts to the corresponding branch based on document type.
- Check whether the
variablesare correctly bound to the batch number and electromagnetic design parameters of the current batch motor, with no cross-batch data confusion. - Simulate a device data pull failure scenario, confirm that the workflow triggers the failure retry mechanism and records exception logs.
- Export the workflow configuration file, verify that the format matches the version requirements of FastGPT v4.17.0.
The values provided are common starting points and should be measured against the reader's own samples.