What this category of data looks like
This category of data primarily comes from logging systems, distributed tracing platforms, Metrics systems, and application SDKs. It is updated daily or when a version is released. Daily supporting documents include logging collection specifications, distributed tracing instructions, metric dictionaries, and dashboard configuration records. Data fields include timestamps accurate to the millisecond, unique session identifiers, user question content, model response content, error stack fragments, distributed trace IDs, and associated metric snapshots. The size of a single log entry typically does not exceed 100 KB.
Constraints imposed on conversation logs and auditing processes
The scattered nature of data sources requires that the conversation log auditing process use chatId and responseChatItemId to associate logs and interaction data across different systems, to avoid isolated troubleshooting clues. Differences in update frequencies require that audit data be archived daily or packaged and retained with versions, to ensure the completeness of cross-version fault retrospectives. The variety of supporting documents requires that configuration items match the field naming rules in logging collection specifications; otherwise, field parsing failures will occur. The multi-field raw data also requires that the auditing process configure field filtering rules to retain only content strongly related to conversation interactions, to reduce the overhead of audit storage and retrieval.
Configuration Guidelines
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId association switch | Enabled | Ensures logs and conversation sessions across different systems are bound, resolving issues with scattered data association |
responseChatItemId log binding | Enabled | Accurately associates individual conversation replies with corresponding observability data, narrowing troubleshooting scope |
| Audit log retention period | 30–90 days | Covers the retrospective cycle of most online faults, while controlling storage costs |
| Field filtering rules | Retain only conversation content and error stacks in the detail field | Reduces redundancy in audit data and improves retrieval efficiency |
| Archiving trigger condition | Triggered daily on a scheduled basis | Matches data update frequencies to ensure timely retention of audit data |
| Log sampling strategy | Full collection of the first 1000 sessions, 10% sampling for remaining sessions | Balances audit completeness and storage overhead |
Three Common Configuration Mistakes
- Symptom: The
detailfield in conversation audit logs is empty, making specific interaction content unviewable. Cause: The logging collection switch for conversation content is not enabled, or the collection fields for conversation content are not configured in the collection specification. - Symptom: Unable to associate corresponding distributed tracing data when troubleshooting across sessions, leading to excessively long fault localization time. Cause: The
chatIdassociation configuration is not enabled, causing logs and conversation sessions across different systems to fail to bind. - Symptom: Retrieval response speed for old conversation windows is significantly slower than for new windows. Cause: Audit logs do not have reasonable field filtering rules configured, causing excessive redundant observability data to be loaded during retrieval.
How to Confirm Configuration Completion
- Manually initiate a test conversation, check whether the corresponding
chatIdanddetailfield content is included in the audit logs. - Import a session identifier from a historical fault, verify whether corresponding logs, distributed tracing data, and metric data can be associated.
- View the audit storage usage, confirm whether it matches the preset retention period and storage threshold.
- Trigger a simulated fault, check whether the corresponding error stack and interaction context can be found in the audit logs.
The configuration values provided here are common starting points. They should be evaluated against the samples specific to the deployment environment.
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.