What the data for this category looks like
The data for this category primarily comes from GitHub or Gitee Issues, code repository version logs, user environment information, and code repository commit records. Daily maintained documents include Issue handling templates, tag rule libraries, historical response records, and problem reproduction description documents. Data update frequency is daily or weekly. Core fields include Issue number, tags, priority, reproduction steps, associated code modules, and processing status. No additional statistical quantitative indicators are included.
What constraints these characteristics impose on workflow orchestration
Data sources are tied to external code repositories. Workflows must support integration with GitHub/Gitee event interfaces for real-time triggering. Data update frequency is daily or weekly. Workflows must be configured with reasonable retry and timeout mechanisms to prevent interruptions from single call failures. Daily documents include fixed fields and rules. Workflows must bind corresponding fields to enable automatic classification and tagging. Core time-consuming steps include problem classification, reproduction information completion, and responsibility module tagging. Workflows must include automated nodes for these steps, plus exception alerts to prevent issue backlogs from slowing processing efficiency.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Rule | Triggered by GitHub Issue creation/update events | Based on data sources being GitHub/Gitee Issues, requires real-time response to newly submitted issues |
variables | Bind Issue number, tags, reproduction steps fields | Based on daily documents including tags, Issue numbers, and reproduction descriptions, used for workflow branch judgment |
nodeId | Bind unique identifiers of classification nodes and alert nodes | Based on the workflow needing to execute different processing logic per node |
| Retry Count | 3 times | Based on daily/weekly data update frequency, prevents process interruption from single interface call failure |
| Timeout Threshold | 600 seconds | Based on typical response times for external code repository interface calls |
| Number of Issues Synced Per Run | Top 20 entries | Based on GitHub API pagination default limit, prevents overload from single requests |
| Message Output Format | Standard Markdown | Based on need to generate reusable problem handling documents |
Three Common Misconfigurations
- When configuring
variablesfor the workflow, failing to bind SQL query result fields causes the AI dialog to not display queried Issue triage results. This occurs because the query dataset is not mapped to the workflow's output variables. - After offline private deployment, the workflow editing interface displays client-side errors. This occurs because the loading path for local static resources is not configured, preventing front-end dependencies from loading properly.
- Tool call failures occur when using the Qwen2.5 model. This occurs because
nodeIdis not correctly bound to the tool call node, so the model cannot recognize the trigger logic.
How to Verify a Successful Configuration
- Manually trigger a GitHub Issue creation event to confirm whether the workflow starts automatically and generates corresponding tag classification records.
- View the
variablesbinding fields of the workflow to confirm that core data such as Issue numbers and tags has been correctly mapped to process nodes. - In the offline test environment of FastGPT v4.17.0, confirm whether the workflow editing interface loads normally.
- Configure a tool call node to confirm whether the model triggers the corresponding execution branch logic as expected.
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.