What the Data for This Category Looks Like
Core data for this category comes from design BOMs, silicon steel sheet and insulating material ledgers, vacuum drying equipment logs, insulating oil test reports, and high-voltage test station records. Daily outputs include winding and core assembly records, vacuum drying records, insulating oil test reports, partial discharge and withstand voltage test reports, and product nameplate parameter tables. Data is updated per single product or each batch of purchased materials. Core fields include insulation distance, moisture content, and tap position parameters. Full-process traceability for single products must be implemented to avoid cross-batch data confusion.
What Constraints These Characteristics Impose on Workflow Orchestration
The requirement for single-product traceability means the workflow must bind a unique product identifier as a core parameter to avoid cross-batch data confusion. Multi-source heterogeneous business data sources mean the workflow must support parallel pulling of different types of documents such as equipment logs, test reports, and test data across multiple nodes. Frequently updated test data means the workflow must be configured with a real-time synchronization trigger mechanism to avoid calling expired data. Strong compliance requirements for insulation and high-voltage tests mean the Q&A link must be associated with the original test data of the corresponding batch, and cannot directly call general knowledge base content.
This page is compatible with FastGPT v4.17.0.
How to Set the Configuration
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
| Variable Reference | Bind the unique product identifier | Match the business requirement of single-product traceability, ensure the Q&A is associated with data from the corresponding batch |
| Knowledge Base Selection | Bind the insulating material and test report library | Cover the core business documents of the category, avoid calling irrelevant content |
variables | Pre-set product ID, test batch number | Provide fixed parameters for subsequent nodes, reduce manual input errors |
| Question Classification Node | Use the initialized model first, then switch to a dedicated fine-tuned model | Adapt to the professional requirements of high-voltage test anomaly classification |
| Retrieval Timeout | 300 seconds | Cover the large-volume data retrieval requirements of transformer test reports |
| Single Recall Count | Top 8 entries | Cover the multi-dimensional process parameter requirements of transformer manufacturing |
Three Common Mistakes
- Workflow variable reference dropdown has no selectable values: The dropdown list for variable reference is empty after a knowledge base is selected in the interface. The cause is that parameters such as product ID and test batch number have not been pre-configured in global variables or the input settings of the current node.
- Question classification node save error: An error prompt pops up when saving or running the workflow when using an initialized AI model. The cause is that the initialized model has not completed basic configuration, or the permissions of the corresponding knowledge base have not been bound.
- AI reply content annotation trigger failure: After configuring stream reply, the annotation node does not execute after the reply is completed. The cause is that the streaming reply switch of the workflow has not been enabled, so the trigger condition is not met.
How to Confirm the Configuration Is Correct
- Manually enter the pre-set product ID to verify whether the variable reference can correctly load the associated data of the corresponding knowledge base.
- Enter the question classification node configuration page to confirm that the configured available models are displayed in the AI model dropdown list.
- Trigger a test run, observe whether the annotation node executes normally after the streaming reply is completed.
- View the workflow node list to confirm that the
nodeIdmatches the configured node identifier.
The values provided here are common starting points and should be measured against the reader's own samples.