What data for this category looks like
Data sources for this category include business system operation records, App permission lists, public privacy policy texts, and user request response records. Target users include privacy compliance teams, security consulting and legal departments. Data update frequency is triggered quarterly or after each product function change. Daily associated documents include personal information lists, processing rule descriptions, impact assessment reports, and user authorization texts. Core fields cover natural person association identifiers, processing purposes, storage periods, and compliance-related nodes. There are no additional custom statistical fields.
What constraints these characteristics impose on the conversation logs and audit link
Decentralized data sources require cross-system association of log entries to ensure full traceability of the natural person information processing lifecycle. Unfixed update rhythms require the audit link to support quick filtering of logs by product change nodes or quarterly cycles. The need to associate multiple types of compliance documents requires logs to bind unique identifier fields of corresponding documents. The special focus on natural person information requires logs to retain identifiable natural person association fields, to avoid missing core objects for compliance review. The serious consequences of errors require log storage to have an immutable verification mechanism, to ensure audit results are traceable and align with daily review workflows of compliance teams.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Log retention period | 90–180 days | Matches quarterly data update frequency and standard compliance review cycles |
| Single log retrieval limit | Top 500 entries | Meets the retrieval needs of compliance teams for batch reviews |
| Log cache duration | 300 seconds | Adapts to temporary storage requirements for large model call logs, avoids invalid data occupying long-term storage resources |
| Log detail field switch | Enabled | Must fully retain the detail field content to cover full-process records of natural person information processing |
| Session ID binding | Enabled | Associates with the chatId field to enable quick retrieval of all compliance review logs associated with a single natural person |
| Response log binding | Enabled | Associates with the responseChatItemId field to bind compliance review results output by the large model to original input data |
Three common configuration errors
- Phenomenon: An error of
cannot read properties of undefinedappears when viewing session log details. Cause: The log collection configuration for thedetailfield is not enabled, so the system cannot read complete content of the corresponding log node. - Phenomenon: Cannot associate the user name of the corresponding natural person when searching compliance logs. Cause: The
chatIdfield for recording user unique identifiers is not bound, so logs cannot be matched with natural person association information. - Phenomenon: Uploaded personal information lists do not participate in compliance review conversations. Cause: The uploaded document is not bound to the
responseChatItemIdfield, so the large model cannot call the uploaded data to complete the review logic.
How to confirm configuration is complete
- Access the FastGPT backend, navigate to the log management page, retrieve sessions with a specified
chatId, and confirm thedetailfield includes complete natural person information processing records. - Trigger a compliance review conversation, check if generated logs bind the
responseChatItemIdfield and associate with corresponding large model output results. - Review the log retention period configuration, confirm the value matches compliance review cycle requirements.
- Export a test log, confirm the format conforms to preset export rules.
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.