What the data for this category looks like
Data for this category originates from content moderation backends, user reporting channels, public regulatory policies, and compliance sample libraries. Daily maintained documents include moderation rules, disposal guidelines, appeal records, and quality inspection reports. Moderation rules include the rule ID, trigger keywords, and effective time period fields. Appeal records include the appeal number, associated content ID, and disposal result fields. Data updates occur daily or in real time when policies change. Single records have no fixed length limit, and align with actual reviewed content.
Constraints imposed on conversation logs and auditing by these characteristics
Multi-source data collection requirements mean the conversation logs and auditing module must support connections to multiple data sources. This avoids switching between tools during troubleshooting. Real-time policy updates for moderation rules require audit logs to retain historical snapshots before and after rule changes. This allows retrospective review of moderation results from the rule’s active time window. The need to cover multiple document field types requires log fields to include identifiers such as rule ID and appeal number. This ensures direct association with daily operational documents during troubleshooting. Variable-length records require log storage to support content with no fixed length. This prevents truncation that would complicate complex sample troubleshooting. In compliance scenarios, audit logs must be retained for a sufficient period. This ensures traceability for violations or false positive incidents.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Log retention period | 30–90 days | Aligns with basic content moderation compliance retention requirements, and covers the time span of standard appeal reviews |
| Multi-source data sync interval | 5 minutes | Matches the rhythm of daily moderation rule updates or real-time policy changes, and ensures audit log timeliness |
| Included log fields | chatId, responseChatItemId, detail | Covers session identifiers, reply identifiers, and full content for moderation conversations, and matches the field structure of daily operational documents |
| Abnormal alert trigger threshold | Number of abnormal entries ≥ 100 per hour | Captures abnormal scenarios such as bulk spread of violating content or false rule judgments in a timely manner |
| Single log storage limit | 1000 MB | Supports storage of long-form moderation conversation content, and prevents content truncation |
| Historical version retention | Enabled | Compatible with the historical snapshot mechanism in FastGPT v4.17.0, and retains audit records before and after rule updates |
Three Common Configuration Mistakes
- Phenomenon: Historical records returned via the log API do not distinguish between users. Individual creators or content cannot be traced via moderation logs. Cause: No association mapping between
chatIdand user identifiers is configured. Logs are not split by user during aggregation. - Phenomenon: Audit logs only display truncated fragments of the
detailfield. Full moderation conversation content cannot be accessed. Cause: Full log storage is not enabled. Long-form moderation conversation content is truncated, which impairs complex sample troubleshooting. - Phenomenon: A max retries error occurs during log pulling, preventing retrieval of complete audit data. Cause: No reasonable multi-source data sync interval is configured. Frequent API calls exceed allowable limits.
How to Verify Proper Configuration
- Run a cross-data source log pull test. Confirm that log data from both the content moderation backend and user reporting channels can be retrieved simultaneously.
- View historical log snapshots. Confirm that audit records from before and after moderation rule changes are retained.
- Cross-check log fields. Confirm that the
chatId,responseChatItemId, anddetailfields are all included. - Trigger an abnormal scenario test. Confirm that abnormal alerts trigger correctly per the configured threshold.
The values listed here are common starting points. Validate against specific samples and use cases for the relevant deployment.
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.