What this type of data looks like
Data sources include WAF, IDS, firewalls, and threat intelligence platforms. Update frequency is daily or when a protection event is triggered. Daily generated documents include rule change tickets, block logs, false positive analysis reports, and policy rollback records. Common data fields include rule ID, session timestamp, source IP, request path, response status code, false positive judgment flag, and session context length. Units are mostly millisecond timestamps, IP strings, and character counts.
What constraints do these characteristics impose on conversation logs and auditing
Multi-source heterogeneous data sources require that conversation logs be stored completely by session dimension, to avoid loss of key information for single protection events. The update rhythm triggered by protection events requires the audit link to associate sessions with their corresponding rule versions, supporting backtracking verification during policy rollbacks. The high-frequency demand for false positive analysis requires retaining complete conversation context and tool call return results, to support root cause location. At the same time, the traceability of protection operations requires audit logs to include complete operation links, including key nodes such as rule configuration changes, session trigger conditions, and response results. This document is compatible with FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Conversation log retention period | 7–30 days | False positive analysis usually requires backtracking sessions from the past 7 days or more, while meeting regular audit requirements |
Associate rule version with chatId | Bind the rule version number of the current session | Policy rollbacks require verifying the session trigger logic of the corresponding version to quickly locate the impact of rule changes |
Record tool return results with responseChatItemId | Enable storage of full return content | Tool call exceptions require backtracking return content to locate parameter or logic errors |
Enable full logs with the detail field | Enable storage of the detail field for all sessions | Long-text task debugging requires complete context to troubleshoot format errors such as offset out of range |
| Maximum number of audit sessions per query | Top 100 entries | Focus on high-frequency protection events to avoid wasting audit resources |
| Session audit scope | Only filter protection-related sessions | Reduce unnecessary log storage and focus on core security operation scenarios |
Three common misconfigurations
- Symptom: When running a long-text protection rule debugging task, the page shows the task completed normally, but the console throws The value of "offset" is out of range error. Cause: Full log storage for the
detailfield is not enabled, so long-text session context is truncated early, exceeding the offset threshold. - Symptom: Testing a patent query via the MCP toolset returns normal results, but calling via the tool call component returns the message that the enterprise has no patent information records. Cause: The business context of the current session is not bound via
chatId, resulting in incorrect query parameters not being carried during tool calls. - Symptom: Conversation logs older than the preset retention period cannot be retrieved. Cause: The conversation log retention period is set too short, not covering the backtracking cycle required for false positive analysis.
How to confirm the configuration is valid
- Retrieve the session record of any protection event, verify that the
detailfield contains complete request and return content. - View the rule version number associated with the session, confirm that
chatIdis bound to the currently active rule configuration. - Trigger a test protection event, check that
responseChatItemIdrecords the complete tool return result. - Adjust the log retention period, verify that session records older than the preset cycle can still be retrieved 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.