This guide is written based on FastGPT v4.17.0.
What this category of data looks like
This category of data primarily comes from Prometheus, Zabbix, cloud monitoring services, application logs, and CMDB systems. Daily associated documents include monitoring metric lists, alert rule documents, on-duty manuals, and alert change records. Data is updated daily or after each alert configuration change. Data fields include monitoring metric names, threshold parameters, alert levels, trigger conditions, notification link configurations, and some fields have clear units. Examples include milliseconds for response time and percentage for disk usage.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Data sources include multiple monitoring and configuration systems, so field formats and naming differ across sources. Multi-turn dialogue must unify context field parsing rules to avoid parameter confusion. Data updates frequently, so prompts must include the latest alert change records and on-duty manual content. This ensures configuration recommendations align with current effective rules. Alert configuration changes require historical traceability, so multi-turn dialogue must use chatId to associate historical context. This maintains conversation link coherence. Additionally, alert configuration fields have clear units. Prompts must explicitly require the model to handle parameters with units to prevent unit errors in threshold settings.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 800–1200 characters | Adapts to the single-page content length of alert configuration documents, avoids exceeding model context capacity limits |
| Question optimization | Enabled | Assists in organizing complex user inquiries about alert thresholds and notification links, distills core requests |
| Historical context binding | Associated by chatId | Retains historical alert configuration parameters from multi-turn dialogue, ensures consistent adjustment recommendations |
| Single response result limit | Top 3 alert rules | Matches the number of alert rules that operations staff review daily, avoids information overload |
| Variable injection scope | Load the last 7 days of alert change records | Provides the model with the latest configuration change data to support generating solutions suitable for current business needs |
| Knowledge base recall scope | Bind the current project's alert configuration library | Ensures the knowledge base content retrieved by the model matches the current operations scenario |
Three Common Misconfigurations
- After calling the dialogue interface, the knowledge base entry ID referenced by this conversation is not returned. Cause: The knowledge base reference log recording configuration for dialogue context is not enabled, and the return parameter for the reference ID is not configured in
variables. - When connecting an external dialogue channel, the output content contains a large number of redundant punctuation marks such as # and *. Cause: The format cleaning rule for dialogue content is not configured, and the Markdown format generated by the model is not processed by the parsing logic adapted to the channel.
- The multi-AI dialogue workflow displays all intermediate node output results. Cause: The result filtering rule for the workflow is not configured, and the output content of all steps is retained by default.
How to Confirm the Configuration Is Complete
- Initiate a multi-turn inquiry about alert threshold adjustment, check if the conversation context retains historical configuration parameters.
- Trigger a multi-node workflow execution, confirm that only the output content of the last AI dialogue node is displayed.
- Call the dialogue interface, check if the returned result includes associated knowledge base reference information.
- Send an inquiry to an external dialogue channel, confirm that the output content does not contain redundant Markdown punctuation marks.
The values provided are common starting points and should be evaluated against samples from the relevant deployment environment.
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.