Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
This category’s data comes from safety production supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems. Updates occur daily. The systems generate daily documents including hazard inspection lists, hazard management ledgers, rectification notices, review records, and major hazard suspension and supervision orders. Data fields include hazard ID, inspection subject, inspection time, rectification responsible subject, rectification deadline, review result, and more. Data is stored as a mix of structured forms and unstructured documents.
What constraints these characteristics impose on conversation logs and auditing
Since data sources include multiple business systems, link conversation logs to the traceability identifiers of corresponding systems to ensure the original data source can be traced during audits. Daily updated business data requires the auditing workflow to archive logs by natural day to avoid mixing cross-day data. The need to link multiple types of business documents requires logs to record the specific document ID and version referenced in conversations. Additionally, due to the high-risk nature of hazard inspection and governance, audit logs must fully retain judgment basis, rectification instructions, and review feedback from conversations, with no key links omitted.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Conversation log retention period | 180 days | Meets general archiving requirements for safety production records, covering the full rectification cycle for hazards |
chatId binding rule | Bind to hazard inspection ID | Ensures each conversation log is linked to the unique identifier of the corresponding hazard, facilitating audit traceability |
detail log enablement status | Fully enabled | Fully records parameter calls and document reference details in conversations to meet audit completeness requirements |
| Log export format | Structured JSON | Adapts to the data import format of safety production supervision systems, facilitating subsequent archiving and analysis |
responseChatItemId association switch | Enabled | Binds the unique identifier of each reply to accurately locate single conversation content required for audits |
| Maximum batch log export count | 50 entries per request | Avoids excessive data volume in a single export, adapting to interface call limits of supervision systems |
Three common configuration errors
- Symptom: An error occurs when calling the API after configuring a token. OneAPI logs show the token field has the fixed value
fastgpt. Cause: The token parameter for conversation logs was not correctly bound, so the actual configured token was not replaced when generating logs. - Symptom: When accessing with the same
chatIdmultiple times, the number of retained historical records varies. Cause: Context retention rules were not uniformly configured, and context parameters across different workflow nodes were inconsistent. - Symptom: Associated IDs for hazard documents are missing from exported audit logs. Cause: The
responseChatItemIdassociation switch was not enabled, so the business document ID referenced in the conversation was not recorded.
How to verify correct configurations
- Initiate a conversation related to hazard inspection and governance, check if background logs include binding information between
chatIdand hazard inspection IDs. - Call the log export API, verify that the exported file contains complete conversation parameters under the
detailfield. - Simulate two conversations using the same
chatId, confirm that the number of retained historical records is consistent across both accesses. - Check log access permission configurations, confirm that only authorized roles can view audit logs for corresponding hazards.
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.