This document is written for FastGPT v4.17.0.
What this category of data looks like
Data sources for this category include raw datasets, ETL logs, annotation platform results, and sampling quality inspection record sheets. Data update frequency is daily or per batch of data. Daily produced documents include standardized cleaning rules, quality inspection sampling tables, abnormal data lists, and final delivery reports. Data fields include data unique identifiers, original content, processing node status, sampling pass rate, exception types, etc. There are no uniform mandatory units, and they are adjusted based on the specific business dataset.
What constraints do these characteristics impose on the workflow orchestration link
Multiple data sources with fixed update rhythms require workflows to support multi-node input to adapt to different data sources, and to configure scheduled or trigger-based startup rules. Diverse daily document types require binding paths or IDs of different documents via variables to avoid configuration chaos. The most time-consuming duplicate data identification and outlier processing links require workflows to support parallel node configuration to improve efficiency. Data errors can lead to delivery failures, so failure jump and alarm nodes must be configured to ensure traceability of exceptions.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Timing | Daily scheduled / Per batch data submission trigger | Matches the daily or per-batch data update frequency |
variables binding rules | Bind cleaning rule IDs, abnormal data list storage paths | Links standardized cleaning rules and result documents used daily |
| Parallel execution node count | 2–4 | Adapts to the most time-consuming duplicate data identification and outlier processing links to improve processing efficiency |
nodeId failure jump configuration | Bind abnormal alarm nodes | Ensures that notifications can be triggered promptly when data processing errors occur |
| Single batch data processing limit | 5000 entries | Matches the business scale of conventional single-batch data |
| Timeout threshold | 1800 seconds | Reserves sufficient time to complete the full cleaning and quality inspection process for a single batch of data |
Three Common Misconfigurations
- Symptom: A 400 status code is returned when calling the API to publish a workflow, and the
modelfield is configured incorrectly. Cause: The task type corresponding tomodelis not clearly defined, and a classification model is mistakenly configured as a question-and-answer model. - Symptom: Workflow response is slow after local deployment, and hardware resource usage is too high. Cause: Parallel node count is not adjusted based on deployment hardware specifications, leading to resource overload.
- Symptom: The abnormal data list has no content after the workflow runs. Cause: The storage path for abnormal data bound to
variablesis not configured correctly, resulting in processing results failing to be written to the specified location.
How to Confirm Proper Configuration
- Review the workflow's trigger configuration to confirm it matches the daily or per-batch data update rhythm.
- Submit a single batch of test data, check whether the cleaning rules bound via
variablestake effect, and whether the abnormal data list is generated normally. - Simulate a processing failure scenario to confirm that the alarm node configured via
nodeIdcan push notifications normally. - Check the resource usage of the deployed hardware to confirm that the parallel node count adapts to the current hardware's processing capacity.
The values provided are common starting points and should be measured against the user’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.