What data for this category looks like
This category of data typically comes from object storage, data warehouses, annotation platforms, and customer acceptance feedback. Updates occur once per batch or per version. Daily generated documents include four types: dataset descriptions, version records, delivery lists, and acceptance reports. These documents support tasks such as dataset version verification and delivery completeness checks during code change reviews. Common fields include version number, sample scope, delivery package path, and acceptance status. There is no unified fixed unit, and requirements must align with the specific scenario of code change reviews.
Constraints imposed on the workflow orchestration link
Since data sources are scattered and updates trigger in batches, workflows for code change review scenarios must connect multiple data source nodes. This ensures pulled dataset versions match submitted code changes. Since document structures are fixed and used for review verification, workflows must automatically check for required fields. This prevents missing key information that would cause review failures. Since errors can lead to non-reproducible training or customer acceptance failures, workflows must bind version traceability logic. This ensures datasets linked to each code change can be traced. Since updates occur once per batch or per version, workflows must trigger on code change submission events. This adapts to the review rhythm of single changes.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Include version number, delivery package path, acceptance status, sample scope | Match required fields from daily documents of this category, used for dataset verification during code change reviews |
| Trigger Condition | Trigger on code change submission events | Adapt to real-time verification needs of code reviews, align with data update rhythm per batch or per version |
| Node Timeout Setting | 600 seconds | Delivery package verification requires traversing multi-source data; default duration cannot cover full process verification |
nodeId | Bind corresponding node IDs of annotation platforms, object storage, and data warehouses | Match multi-source characteristics of this category's data, ensure corresponding datasets can be pulled during reviews |
| Delivery Document Verification | Verify the first 4 category documents | Cover four core review documents: dataset descriptions, version records, delivery lists, and acceptance reports |
| Delivery Package Size Limit | Within 1000 MB | Adapt to data transmission bandwidth requirements in conventional code review scenarios |
Three common misconfigurations
- A workflow returns a "missing fields" error after running. This occurs when required fields such as version number and delivery package path are not configured in
variables, and the setup does not align with fixed dataset verification requirements in code reviews. - A workflow does not trigger on code change events, resulting in unchanged dataset versions during reviews. This occurs when the trigger condition is mistakenly set to scheduled triggering, rather than adapting to the review rhythm of single changes.
- AI node output includes non-review-related verification logic. This occurs when the prompt is not limited to only verifying content related to dataset delivery and version, mixing in irrelevant business verification rules.
How to confirm the configuration is complete
- In the FastGPT v4.17.0 workflow editor, manually trigger a simulated code change submission. Check whether the workflow starts automatically and completes dataset verification.
- View workflow run logs. Confirm that fields such as version number and delivery package path in
variablesare correctly read and passed. - Check whether data source nodes bound by
nodeIdare properly connected. Confirm that the delivery package of the test dataset can be pulled. - Configure the AI node's prompt to only verify dataset delivery documents. Verify whether the output only includes review-related verification results.
The values provided here are common starting points, and should be measured against relevant in-use 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.