What the data for this category looks like
This category of data is generated around product usage behavior. Data sources include tracking systems, data warehouses, CRM systems, and ticket systems. Update frequency is typically daily or weekly. Daily produced documents include usage analysis reports, tracking plans, indicator caliber tables, and dashboard requirement forms. Common data fields include session identifiers, user-associated information, interaction content, operation time, response results, and more. There are no uniform mandatory units, and matching must be done based on specific indicator calibers. The core goal is to deliver cross-customer function and behavior insights.
What constraints these characteristics impose on the "conversation logs and auditing" workflow
Multi-source data must first complete identifier alignment to ensure cross-system traceability of session logs. Daily or weekly update cycles require auditing processes to match data refresh periods, avoiding data lag or disconnection. The rich daily document system requires audit fields to stay consistent with indicator caliber tables and tracking plans, preventing indicator misjudgment. The cross-customer behavior insight goal requires auditing to distinguish sessions of different users and customers, while avoiding data leakage risks.
How to configure settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
chatId | Globally unique and persistent | Matches session identifiers from tracking systems and data warehouses to ensure cross-system traceability |
responseChatItemId | Bound to single-round interaction content | Corresponds to the unique identifier of a single conversation log, enabling precise location of abnormal interactions |
detail | Retain complete interaction text and metadata | Covers fields such as user input, AI responses, and operation time to meet auditing requirements |
| Session user isolation configuration | Calibrated based on actual testing | Distinguishes historical conversations of different users, adapting to scenarios where multiple users share an application |
| Log storage period | 30–90 days | Meets conventional retention requirements for compliance reviews, balancing storage costs and auditing needs |
| Abnormal audit threshold | Abnormal interaction proportion ≥10% | Based on abnormal troubleshooting needs from daily analysis reports, enabling rapid location of risky sessions |
Three common misconfigurations
- Phenomenon: The console cannot view console.log logs from code running modules. Cause: Detailed metadata collection for the
detailparameter is not enabled. Only core session fields are stored, and code running output content is not included. - Phenomenon: Conversations created via API cannot be viewed on the frontend page. Cause: The associated mapping between
chatIdand page sessions is not configured. Cross-end data cannot interoperate, and only the session identifier from the API side is returned. - Phenomenon: The Human field is empty in response record previews. Cause: User input collection configuration bound to
chatIdis not enabled. Only AI response content is stored, and original text of user interactions is not retained.
How to confirm configurations are set correctly
- Initiate a test conversation, check if a globally unique
chatIdidentifier exists on the session details page. - Call the API to create a session, check if the returned result includes the
responseChatItemIdfield and completedetailmetadata. - Configure a multi-user test environment, verify that historical conversations of different users are only visible to themselves, with no cross-user data leakage.
- View the log storage directory, confirm that the storage period meets the 30–90 day configuration requirement.
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.