Data Characteristics
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints for Workflow Orchestration
Since metal material processing ledger data originates from multiple systems and is updated per production batch, workflows must be configured with multiple data source nodes to connect to ERP systems and online testing equipment. Workflows must also trigger tasks per batch to avoid duplicate summarization. Ledger fields contain numeric data with multiple units, so a standardization step must be added after data extraction to unify unit formats for subsequent report generation. Each ledger has a large number of fields, so variables must be used to preset core business variables such as heat/lot number and element qualification thresholds, for use in subsequent qualification judgment and content filtering. Production data has high real-time requirements, so timeout settings between workflow nodes must match the delay duration of device data uploads.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Data Source Connection Configuration | Connect to PostgreSQL database and online testing equipment APIs | Matches the multi-source data origin of metal material processing production ledgers |
| Trigger Node Rules | Match production batch completion events by nodeId | Adapts to batch-updated data characteristics to avoid duplicate summarization |
| Global Variable Presets | variables includes heat/lot number, element qualification thresholds | Used for conditional judgment in subsequent report generation |
| Data Extraction Fields | Select heat/lot number, carbon content, tensile strength, qualification flag | Matches core business fields of production ledgers |
| Batch Extraction Quantity | Top 100 records | Adapts to the average number of records per production batch ledger |
| Node Timeout Threshold | 600 seconds | Adapts to the average data upload delay duration of production equipment |
| Plugin Call Order | Database extraction first, then AI report generation | Ensures the latest ledger data is used for report generation |
Three Common Misconfigurations
- Workflow validation fails with "Missing or null values": The symptom is a validation failure popup appears after running the PostgreSQL database connection plugin, with no specific error message displayed. The cause is incorrect configuration of the database connection address, port, and authentication parameters, and failure to preset environment variables required for database connections in
variables. - AI model input error with prompt "chat:aiinputis_e": The symptom is an error is triggered after passing the code running node's result to the AI model. The cause is failure to correctly bind the result variable to the AI model's input parameters, resulting in empty or abnormally formatted input content.
- Text extraction node produces no clear results: The symptom is no expected summary content is generated after extracting ledger data. The cause is failure to specify correct extraction fields and filtering conditions based on the field characteristics of metal material processing ledgers, leading to invalid data being included in the summary.
How to Verify Successful Configuration
- Manually trigger the workflow once, check the output logs of the data source node to confirm that the extracted ledger fields match the preset fields.
- Run the data standardization node to check whether the field formats after unit conversion meet business requirements, with no abnormal characters or unit errors.
- Trigger the AI report generation node to check whether the output result includes the preset ledger summary content and qualification judgment logic.
- Simulate repeated triggering of the workflow for the same batch to confirm that no duplicate report entries are generated and the trigger rules function correctly.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.