What This Category of Data Looks Like
Data sources for this category include monitoring systems, ticket systems, contract documents, and customer communication records. Update cadence follows monthly service report generation and quarterly customer review sessions. Daily documents include SLA clauses, monthly service reports, availability statistics, and customer review minutes. Core fields include service cycle identifier, SLA metric agreed values, abnormal event timestamps, attribution results, and customer conversation association IDs. Data is only archived by service cycle and contract agreement dimensions, with no fixed statistical values.
What Constraints These Characteristics Impose on Conversation Logs and Auditing
Since data updates monthly or quarterly, conversation log auditing does not require high-frequency real-time collection. It must accurately link to SLA data for the corresponding service cycle. Multi-source data integration requires the auditing workflow to map conversation log fields to those from ticket, contract, and other systems. This prevents cross-cycle data confusion. Documentation focuses on service performance summaries. Auditing must verify that conversation content complies with contracted response times and service standards. It must also ensure customer sensitive information is not leaked. The most time-consuming SLA metric calculation and abnormal attribution steps require auditing to trace complete interaction context. This ensures calculation basis is accurate.
How to Configure Settings
This guide applies to FastGPT v4.17.0
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the ticket ID associated with the service cycle | Used to accurately link conversation logs to SLA data for the corresponding service cycle, narrowing the auditing scope |
responseChatItemId | Only record core interaction items of AI replies and user questions | Reduce redundant logs, focus on service response links related to SLA |
detail | Enable complete interaction detail recording | Used for complete context verification during abnormal event attribution |
| Conversation log retention period | 180 days | Matches the auditing cycle requirements of monthly/quarterly service reports |
| Abnormal log trigger threshold | 10 minutes beyond the SLA-agreed response time | Precise condition for triggering audit tracing |
| Audit log sampling rate | Full collection | Meets complete record requirements for compliance reviews |
Three Common Configuration Errors
- Phenomenon: Exception errors thrown by the system do not appear in conversation logs. Cause: The complete recording configuration of the
detailparameter is not enabled. This causes system-level exceptions not actively triggered by users to not be captured. - Phenomenon: Obtained conversation record IDs cannot be linked to SLA data for the corresponding service cycle. Cause: The
chatIdis not bound to the ticket or service cycle identification field. This prevents matching corresponding service SLA metrics during auditing. - Phenomenon: SLA metric calculation results in monthly service reports do not match log records. Cause: Conversation logs are not filtered by service cycle. This causes cross-cycle data to be incorrectly included in statistics.
How to Verify Correct Configuration
- Call the conversation log query interface. Check that the returned results include the
chatIdfield and that it matches the preset service cycle identifier. - Simulate a user interaction that exceeds the SLA-agreed time limit. Check that the log records complete interaction details and exception prompts.
- Test the offset parameter of the "Get Conversation Record List" interface. Confirm that target logs can be obtained in pages by service cycle.
- View the log storage retention period configuration. Confirm that it meets the monthly or quarterly auditing cycle requirements.
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.