What this category of data looks like
This category of data primarily comes from system logs, interview records, asset inventories, and security device logs exported by assessment tools. It is updated per individual project or annually. Core documents include classification determination reports, gap analysis reports, rectification plans, and assessment reports. Data fields include project identifiers, assessment control item numbers, conversation interaction times, operating subjects, conversation content, and audit results. Single-project data volume varies with the expansion of the assessment scope.
What constraints these characteristics impose on the "conversation logs and auditing" link
This category of data is updated per individual project or annually. Conversation logs must be isolated by project to avoid cross-project data confusion. Core documents are strongly linked to conversation content. The auditing process must retain complete conversation chains and operation traces to ensure traceability. Single-project data volume varies with the assessment scope. The system must support dynamic adjustment of log storage thresholds per project. It must also comply with auditing specifications for cybersecurity level protection assessments. Conversation logs cannot be deleted arbitrarily. All interaction and operation behaviors must be fully recorded.
How to set the configuration
This configuration applies to FastGPT v4.17.0 and later versions.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the project's unique identifier | This category manages data per project. Use chatId to link conversation logs to their corresponding project, enabling data isolation and traceability |
responseChatItemId | Bind to the assessment control item ID | Core documents are linked to specific assessment control items. This parameter links conversations to audit content for specific control items |
detail | Enable full log collection | Auditing requires complete interaction details. Full detail captures all conversation content to meet traceability requirements for cybersecurity level protection assessments |
| Log storage threshold | 1000 MB, calibrated via actual testing | Single-project data volume changes with assessment scope. This threshold adapts to log storage needs for most medium-sized assessment projects |
| Log retention period | Extend per project cycle | Aligns with the project's rectification and review cycle to ensure audit logs cover the full assessment process |
Three common configuration mistakes
- Symptom: Calling the conversation deletion interface returns a failure. Specified logs for
chatIdcannot be deleted. Cause:chatIdis not bound per project. Cross-project log operations are performed incorrectly, leading to permission verification failure. - Symptom: The model returns an empty response after being called. No clear error message appears in server logs. Troubleshooting: Server environment variables
AIPROXY_API_ENDPOINT(root address of the AI Proxy service) andAIPROXY_API_TOKEN(AI Proxy administrator token) are not configured correctly. This causes service startup verification to fail. - Symptom: Only partial conversation snippets are displayed in audit logs. Complete interaction content cannot be viewed. Cause: Full
detaillog collection is not enabled. Only condensed conversation summaries are stored, which fails to meet audit traceability requirements.
How to confirm the configuration is complete
- Review server environment variable configuration. Confirm
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare set correctly. Do not append/v1or specific interface paths. - Initiate a test conversation. Query logs for the corresponding project using
chatId. Confirm logs are fully linked to the project identifier, with no cross-project data confusion. - After enabling the
detailparameter, view conversation log details. Confirm complete interaction content has been fully recorded. - Attempt to delete logs for a specified
chatId. Confirm permission verification passes and the deletion action executes normally.
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 2026-10-19.