What data in this category looks like
Typically, data in this category consists of dataset packages intended for subsequent model training or customer acceptance. These packages originate from object storage, data warehouses, annotation platforms, and customer acceptance feedback. The update cadence is once per delivery batch or per version. Supporting documents include dataset descriptions, version records, delivery lists, and acceptance reports. Core fields include version number, delivery batch identifier, sample unique identifier, acceptance status, and update time. No additional custom statistical units are used.
What constraints these characteristics impose on conversation logs and auditing
Since data is updated by batch and version, auditing must bind to the corresponding delivery batch and version identifiers to ensure traceability of dataset versions invoked during conversations. Since data sources are scattered, logs must record invocation information for each data node to avoid mixing data from different sources. Since supporting documents include multiple types of version records, auditing must associate conversation logs with the corresponding version documents to facilitate checking sample differences and delivery completeness. Since the most time-consuming step includes version number management, auditing must verify version number consistency to prevent incorrect invocation of data from different batches, and avoid issues such as non-reproducible training results or customer acceptance failures.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Conversation ID Binding | Associate with delivery batch unique identifier | Used to associate conversations with dataset delivery batches and enable cross-session version traceability |
| Session Entry ID Binding | Bind to dataset sample unique identifier | Used to locate specific sample data invoked during conversations and facilitate checking sample differences |
| Full Log Switch | Enable | Includes dataset version number, source node information to meet audit traceability requirements |
| Log Retention Period | 180–365 days | Matches the conventional traceability period for compliance reviews and covers the full cycle of version iterations |
| Maximum Number of Log Entries per Query | Top 1000 entries | Avoids loading excessive data during audits and improves query efficiency |
| Version Verification Switch | Enable | Used to verify that the currently used dataset version matches the delivery list before conversation invocation |
Three Common Misconfigurations
- Symptom: Unconfigured external link title content appears in conversation logs when calling via API. Cause: Non-dataset-related invocation parameters were not excluded in log filtering rules, leading to irrelevant content being mixed into audit logs.
- Symptom: Unable to locate dataset samples from a specific batch during audits. Cause: Session entry IDs were not bound to sample unique identifiers, breaking the association between conversation sessions and data samples, making it impossible to check sample differences.
- Symptom: Compliance reviews fail due to insufficient log retention duration. Cause: Log retention period was not set in accordance with compliance requirements, only short-term logs were retained, failing to cover the full version iteration cycle and making historical delivery data untraceable.
How to Confirm Proper Configuration
- An operator may log in to the FastGPT v4.17.0 admin backend, navigate to the conversation log configuration page, and check whether conversation IDs have been bound to delivery batch unique identifiers.
- A test call may be initiated, and the generated logs may be verified for inclusion of core audit fields such as dataset version number, sample unique identifier, and data source node.
- The log retention period may be checked to confirm it is set to 180–365 days, meeting the basic requirements for compliance traceability.
- A dataset version verification may be triggered, and the system may be confirmed to automatically verify that the currently used dataset version matches the delivery list during invocation.
The values provided here are common starting points and should be measured against the operator’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.