What data for this category looks like
Data for this category comes from crowdsourcing platforms, annotation quality feedback systems, staff training exam records, and financial settlement systems. Update frequency is typically daily or monthly. Core documents include staff access rules, pre-job training materials, monthly performance reports, and settlement statements. Common data fields include unique staff identifiers, associated annotation task numbers, quality inspection scores, training assessment results, settlement amounts, and settlement cycles. The data covers full-process flow for staff management and annotation operations.
Constraints imposed by these characteristics on conversation logs and auditing
Dispersed data sources and inconsistent update rhythms require conversation logs to link staff identifiers, task numbers, and financial settlement fields simultaneously. This ensures full annotation chain traceability during audits. Static staff access and training data requires logs to retain historical versions. This prevents traceability failures caused by staff information changes. Monthly updated settlement data requires audit logs to support filtering by settlement cycle. It also requires compatibility with real-time verification of daily quality inspection data. Field differences across multiple data sources require audit configurations to uniformly map identifier fields from different systems. This avoids broken associations that reduce audit accuracy.
How to set up configurations
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Use a composite prefix format that binds staff ID and annotation task ID | This category requires simultaneous traceability of staff and tasks. The composite format enables two-way association, matching the field mapping needs of multi-source data |
responseChatItemId | Retain the full ID of the complete conversation chain | Full interaction of a single annotation conversation must be recorded. The full ID accurately locates conversation fragments linked to individual quality inspections or settlements |
detail | Enable log storage with 1000–2000 characters | Annotation conversations for this category include task instructions and annotation results. Sufficient length preserves complete business context, meeting compliance audit requirements |
| Log retention period | Set to twice the length of the settlement cycle, with a maximum of 365 days | Settlement data updates monthly. Twice the cycle covers a complete settlement audit cycle, aligning with industry general retention requirements |
| Multi-source field mapping | Preset standard mapping rules for staff ID and task ID | Data for this category comes from multiple systems. Unified mapping prevents broken log associations, improving audit efficiency |
Three common configuration errors
- Symptom: Empty results return when filtering historical conversation logs by entering a specified staff ID in the audit module. Cause: The
chatIdconfiguration is not set to the format that links staff ID and task ID, so logs cannot be filtered by staff identifiers. - Symptom: Empty operational data appears in conversation logs after calling the interface to generate a workflow audit report. Cause: Full log collection for the
detailconfiguration item is not enabled. Only basic conversation identifiers are retained, and workflow execution details are not recorded. - Symptom: Incomplete content appears in the
detailfield of a single conversation’s log details. Cause: The configured log storage character length is lower than the actual content length of the annotation conversation, so complete context cannot be obtained during compliance audits.
How to confirm correct configuration
- Manually enter a known staff ID and associated task ID, filter the corresponding conversation logs, and confirm that matching result lists return.
- View the log details of a single conversation, confirm that the
detailfield contains complete annotation instructions, interaction content, and workflow execution details. - Call the audit interface, pass a valid
responseChatItemId, and confirm that the returned log data has no missing or truncated fields. - Check the log retention period configuration, confirm that the retention period covers at least one complete monthly settlement cycle.
The values provided here are common starting points. They should be evaluated against the operator’s own sample data.
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.