What this category of data looks like
This document is compatible with FastGPT v4.17.0. Data for this category comes primarily from cloud audit logs, IAM permission configurations, CMDB resource inventories, and security baseline scan results. Data is stored in structured table formats. Four standard document types are generated regularly: compliance checklists, permission audit reports, resource inventory lists, and rectification tracking forms. Fields include resource identifiers, permission configuration items, compliance status, rectification progress, and additional related items. Data is updated via monthly or quarterly batch synchronization, with no real-time streaming update scenarios.
Constraints on multi-turn dialogue and prompt engineering
The low monthly or quarterly data update frequency means multi-turn dialogue context length does not need to be excessive. Limit the valid range of historical messages to avoid interference from redundant information. The predominantly structured data format requires prompts to clearly define rules for field extraction and compliance status verification. This prevents the large model from generating unstructured, vague conclusions. Multiple data source inputs require clear context boundary separation for IAM permissions, CMDB resources, and baseline scan data during the dialogue flow. This stops cross-source data confusion. For the most time-consuming inventory and anomaly identification stages, dialogue must focus on specified document types to avoid loading irrelevant data.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Retain the first 3 rounds of dialogue context | The low update frequency of data in this category means redundant historical messages will not affect judgments for current audit tasks |
chatId | Bind the unique identifier of the current compliance audit task | Distinguish dialogue contexts for different audit projects, avoiding cross-task data confusion |
variables | Bind field mapping variables for compliance checklists and resource inventories | Accurately reference fields such as resource IDs and compliance status in structured data |
| Maximum context character count | 800–1200 characters | Data in this category is predominantly structured tables, so single-round input does not need to be lengthy |
| Historical message automatic cleanup threshold | Clean up the earliest non-core messages when context exceeds 1000 characters | Reduce invalid context occupancy and improve dialogue response speed |
| Prompt template | Calibrate based on actual testing | Verification rules vary across compliance scenarios, and must match corresponding business documents |
Three Common Misconfigurations
- Phenomenon: A dialogue node returns an input length limit exceeded error without prior interception. Cause: No context length limit is configured, and all historical messages are passed directly to the large model interface, exceeding the large model's input limit.
- Phenomenon: Dialogue request results do not include a reference ID field. Cause: No data source reference identifier is bound in
variables, and no rule for referencing original compliance data in results is configured. - Phenomenon: Multi-turn dialogue cannot recall abnormal resource IDs from prior audit stages. Cause: No unique context for the current compliance audit task is bound via
chatId. This resets the session state during each dialogue, losing historical information.
How to Verify Correct Configuration
- Submit a segment of historical dialogue exceeding 1000 characters, then check if the system automatically cleans up the earliest non-core messages without triggering an input limit exceeded error.
- Initiate a dialogue that uses multiple data sources (IAM, CMDB, baseline scan), then check if the large model can accurately distinguish and reference content from corresponding fields.
- Create a new compliance audit task, then confirm that the new dialogue's context does not mix with historical messages from old tasks.
- Review dialogue return results, then confirm that the results include reference identifiers for the corresponding original data.
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.