What this category of data looks like
Data sources consist of standardized texts from power grid enterprises’ internal party building knowledge bases, daily party member learning interaction records, and original Q&A interaction data. Updates occur in real time. Each single Q&A interaction is written to storage immediately after generation. The document structure follows a single conversation unit, including chatId, user identifier, question content, AI reply content, interaction timestamp, and conversation status field. The timestamp unit is milliseconds. The conversation identifier field uses string format. The content field unit is characters.
Constraints on conversation logs and auditing
Real-time updated data sources require zero latency in the conversation log writing link. Otherwise, real-time audit requirements cannot be met. The multi-field conversation structure requires full interaction content of a single conversation to be associated via chatId during audits. This avoids traceability gaps caused by split conversations. The string-format conversation identifier field needs unified verification rules. This ensures identifier consistency across module calls. The timestamp field must match the power grid enterprise’s standard time zone. This ensures the accuracy of the audit timeline. Additionally, compliance requirements for party building content require log storage to have tamper-proof characteristics. This meets internal audit compliance requirements.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Composed of session initiation timestamp + random 6-digit string | Ensure a single conversation has a globally unique identifier, avoiding confusion across session interactions |
responseChatItemId | Bound to the corresponding chatId, then numbered in interaction order | Accurately associate a single AI reply with its parent conversation, making it easy to quickly locate specified interaction content during audits |
detail | Enable full log collection | Fully record all details of user questions and AI replies, meeting audit traceability requirements for party building learning Q&A |
| Log storage period | 180 days | Complies with internal audit log retention specifications for power grid enterprises |
| Log alert threshold | Trigger an alert when the abnormal interaction ratio exceeds 5% | Timely detect abnormal interaction behaviors in party building learning Q&A, supporting compliance audits |
| Log retrieval index | Establish a joint index based on chatId and interaction timestamp | Improve retrieval speed in audit scenarios, quickly filtering logs for specified conversations or time periods |
Three common configuration mistakes
- Symptom: Unable to retrieve the conversation history of a specified session in MongoDB storage. Cause: No unique index was created for the
chatIdfield, leading to duplicate session identifiers or index failure, making accurate matching of the target session impossible. - Symptom: Some AI reply content is missing from audit logs. Cause: The
detailfull log collection configuration was not enabled, only basic interaction identifiers were collected, and complete reply details were not recorded. - Symptom: Timeout errors occur during log retrieval. Cause: No joint index was created based on
chatIdand interaction timestamp, full table scanning causes retrieval timeouts, failing to meet real-time query requirements for audits.
How to confirm the configuration is applied correctly
- Initiate a party building learning Q&A interaction, retrieve the corresponding storage medium via
chatId, and confirm complete interaction log content can be obtained. - Access the FastGPT log management interface, select the corresponding conversation, and verify that logs include full details of user questions and AI replies.
- Adjust the log storage period configuration, and confirm the storage system automatically performs archiving or cleanup operations per the set rules.
- Simulate an abnormal interaction scenario, and confirm the log alert rule triggers the corresponding notification per the set threshold.
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 2026-09-28.