What the data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What Constraints These Characteristics Impose on the "Conversation Logs and Auditing" Link
The structured characteristics of party building learning materials require that conversation logs must be associated with the unique identifier of the corresponding Q&A material. This ensures that official basis can be traced back during audits. The quarterly update rhythm of materials means logs do not need to synchronize the full material library frequently. Only associated identifiers need to be synchronized after material updates. The usage scenario of user-defined UID requires the log system to support filtering conversations by custom identifiers. This meets the auditing needs of government and enterprise units by department and user level. The inference time consumption field in conversation logs must accurately record the response duration of a single round of interaction. This adapts to the effect review requirements of party building learning.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
detail | Full | Party building auditing requires covering all interaction details, including intermediate inference steps and official basis association information |
chatId | Generate unique value based on user-defined UID | Adapt to the auditing needs of government and enterprise units to filter historical conversations by department and user level, matching the usage scenario of custom user identifiers |
responseChatItemId | Retain all conversation nodes | Ensure that the complete link of a single round of interaction can be traced during auditing, avoiding missing key Q&A links |
| Conversation log retention period | 180 days | Meet the conventional cycle requirements of internal auditing for government and enterprise units, covering the time range of quarterly reviews and compliance checks |
| Log export format | CSV format | Adapt to the data import format of internal auditing systems for government and enterprise units, simplifying the auditing process |
| Inference time consumption recording | Calibrate based on actual measurements | The response duration of party building Q&A is affected by material complexity, and the recording accuracy needs to be adjusted based on actual deployment scenarios |
Three Common Mistakes
- Phenomenon: After initiating a Q&A request with a custom UID, the returned historical conversation does not match the interaction records of the corresponding custom user. Cause: The custom UID is not correctly bound to the
chatIdgeneration logic, resulting in a disconnect between the conversation identifier and the user identifier. - Phenomenon: When batch exporting the preset 100 party building Q&A data entries, the number of obtained inference time consumption results does not match the expected number. Cause: Precise scene filtering conditions are not set, resulting in mixing interaction logs from non-party building learning scenarios.
- Phenomenon: The exported conversation logs do not include the inference time consumption data of a single round of Q&A. Cause: The full recording switch of the
detailconfiguration item is not enabled, or the log collection logic does not associate the inference time consumption field.
How to Confirm the Configuration Is Complete
- Initiate a Q&A request with a custom UID, check whether the returned historical conversation only includes the interaction records of this custom user, and confirm that the
chatIdbinding logic takes effect. - Export the log file of a single round of conversation, verify whether it includes complete interaction details and inference time consumption data, and confirm that the
detailconfiguration item is enabled. - Retrieve the conversation of the custom UID of a specified department, confirm that the log system supports filtering data by custom identifiers to meet auditing requirements.
- Attempt to delete the specified conversation record, verify whether the front-end display and back-end stored conversation data are cleaned synchronously, to ensure the integrity of the auditing link.
The values provided here 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 2026-09-28.