What the data for this category looks like
This category of data originates from incident timelines, system logs, monitoring metrics, and code commit records. Four types of documents are routinely produced: RCA reports, incident tickets, review minutes, and rectification tracking sheets. Document fields include incident occurrence time, root cause classification, rectification measures, tracking status, and more. Time fields use standard time formats. Rectification tracking status is an enumeration type. Data updates occur once per major incident or once per month. The content volume of a single document varies based on incident complexity, with no fixed uniform length.
Constraints Imposed on Workflow Orchestration
Data sources for this category are scattered. Multiple nodes must be configured in the workflow to pull information from different sources. Use variables to uniformly associate identification parameters for each node, to avoid data flow confusion. Data update frequency is low. The workflow does not need frequent triggering, and works well with incident archiving or scheduled task triggers. Most document structures are cross-linked. The workflow must support field matching and aggregation across documents. It must also ensure consistent field formats across all steps, to reduce manual verification work. The most time-consuming evidence collection step requires automation. Add built-in data aggregation and cleaning nodes to the workflow to reduce manual operation costs for the team.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Variable reference settings | Bind the incident ID, knowledge base ID, and file collection identifier in variables | Match the associated query requirements for multi-source data |
| Node ID binding | Bind the nodeId of the log extraction node and monitoring pull node | Ensure correct workflow node execution order and data flow |
| Knowledge base search scope | Specify the target file collection | Narrow the search scope and improve root cause location efficiency |
| LLM temperature setting | 0.6–0.8 | Balance the accuracy and flexibility of root cause analysis |
| Execution timeout threshold | 1200 seconds | Adapt to the time consumption requirements of multi-source data collection |
| Batch execution trigger condition | Triggered by the incident archiving completion event | Match the monthly update frequency requirement |
Three Common Configuration Errors
- Phenomenon: The full process completes during online debugging, but batch execution nodes fail to finish when called via API. Cause: The callback waiting rule for API triggering was not configured for the batch execution node in the FastGPT v4.17.0 workflow configuration.
- Phenomenon: The knowledge base search node cannot specify a target file collection for searches. Cause: The corresponding file collection identifier parameter was not bound via
variables, leaving the search scope unspecified. - Phenomenon: The temperature parameter cannot be set when calling the LLM. Cause: No temperature value variable was configured in
variables, and the LLM node without configured parameters was called directly.
How to Verify Correct Configuration
- Confirm that necessary parameters including incident ID, knowledge base ID, and file collection identifier are bound in
variables. - Check that node ID bindings match the actual IDs of each data collection node in the workflow.
- Verify that the batch execution node's trigger condition matches the incident archiving event node.
- Run a full-process debug in FastGPT v4.17.0 to confirm normal data flow across all links.
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.