What the Data for This Category Looks Like
Data sources for this category include raw datasets, ETL logs, annotation platform output results, and sampling inspection results. Data update frequency follows a daily or per-batch task schedule. Associated daily documents include cleaning rule configuration tables, quality inspection sampling record sheets, abnormal data lists, and delivery quality reports. Data fields cover raw data identifiers, cleaning operation marks, anomaly type codes, sampling pass rates, and other fields. There is no unified mandatory unit, and adjustments are made based on business scenarios.
What Constraints These Characteristics Impose on the "Forms and Interactions" Link
Multiple data sources and flexible update rhythms require external business systems to implement multi-data source batch binding and scheduled trigger configuration. Diverse daily document types require external business systems to implement field mapping configuration for different document templates, to avoid cleaning failures caused by field mismatches. Single batch data processing volume may fluctuate. External workflows or system configurations are needed to limit the single-batch submission cap in the interaction link, preventing resource overload. Abnormal data lists and sampling results require real-time synchronization. External business systems must implement real-time associated verification to ensure configuration consistency with the latest quality inspection data.
The following configurations are based on FastGPT v4.17.0.
How to Set the Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Form Input Node formInput | Test example: Select 3 fields from data cleaning and quality inspection as required inputs; actual fields are determined per task | This node collects user input; missing required items requires troubleshooting interaction status to avoid subsequent nodes failing to obtain complete input |
User Selection Node userSelect | Test example: Set 2 business options, check corresponding branches one by one | This node handles user selection; queries and filtering of data cleaning and quality inspection related records must be implemented by subsequent interfaces or external systems |
| Post-Submission Field Passing | Test example: Prepare 10 data cleaning and quality inspection input samples, covering complete filling, missing required items, and canceled operations | Compare input and node output to verify whether subsequent nodes receive corresponding fields; result thresholds are determined per actual task |
| External Implementation Item: Default File Format Selection | Must be implemented separately by external systems; All recognizable formats | Adapt to multi-data source docking requirements, must be implemented via external system docking configuration |
| External Implementation Item: Data Source Selection | Must be implemented separately by external systems; Multiple data source options provided by external business systems | Cover multiple data sources such as raw datasets and ETL logs |
| External Implementation Item: Scheduled Trigger Cycle | Must be implemented separately by external systems; Daily/per batch task rhythm | Match data update frequency, must be implemented via external system or workflow configuration |
Three Common Mistakes
- Phenomenon: Unable to select all recognizable formats by default when uploading files; manual selection of corresponding formats is required when docking with MCP. Cause: No default file format selection rule configured for external data source docking, failing to adapt to multi-data source docking requirements.
- Phenomenon: After adding external model call configuration, an empty error is returned when entering tokens, and the interface displays an interface request failure prompt. Cause: No token legitimacy verification rule configured in the external business system or subsequent nodes, and the verification logic for corresponding parameters is not bound.
- Phenomenon: Unable to select knowledge base reference variables in the code running node, and search results cannot be output. Cause: No correct variable reference rule configured for the corresponding workflow node, and the mapping relationship of the corresponding data source is not associated.
How to Confirm the Configuration is Correct
- Enter the external data source docking configuration page, check the default file format option settings, and confirm adaptation to the current docking data source format requirements.
- Submit a test batch of data, verify that the trigger rule matches the data update cycle, and no abnormal error prompts are returned.
- Test external model token input, confirm that the external verification rule takes effect, and no empty error is returned.
- Enter the corresponding workflow node configuration page, check whether the knowledge base binding field has been added, and the corresponding reference variable can be selected normally.
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 2026-10-19.