What the data for this category looks like
Web frontend engineers, mobile frontend teams, and mini-program development teams typically handle daily data including frontend page load logs, API call records, user interaction events, error stacks, browser user agent information, session identifiers, and more. Data sources include build logs from code repositories, call records from API management platforms, real-time frontend logs captured via browser consoles, and version release manifests. Daily documentation covers API documentation, component specifications, joint debugging records, and release manifests. Data is typically updated daily in aggregate or alongside version releases. Fields include session ID, request path, response status code, error time, browser type, and others. There is no unified mandatory format, and data must be organized according to the collection rules of the frontend project.
What constraints do these characteristics impose on conversation logs and auditing?
The multi-source data characteristics of frontend application development typically require conversation logs and auditing workflows to integrate multi-link data including code repositories, API platforms, and browser logs, to enable cross-source fault retrospective. The fact that data updates accompany version releases requires auditing to support filtering logs by version, to match troubleshooting needs during joint debugging and post-release. Frontend data includes detailed fields like browser UA and session identifiers, requiring auditing to associate session identifiers such as chatId to enable precise troubleshooting by user session. Daily documentation primarily consists of joint debugging records and release manifests, requiring audit logs to be bound to version information to facilitate linking faults to release changes. The most time-consuming compatibility troubleshooting step requires auditing to support quick filtering of logs by browser type and page path, to shorten troubleshooting cycles.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the frontend page session | Enable association between frontend user sessions and audit logs, supporting fault retrospective by session |
detail | Enable full log storage | Frontend errors include details such as stacks and browser information; full storage facilitates compatibility troubleshooting |
| Log retention period | 30 days | Matches frontend version iteration and joint debugging cycles, balancing storage costs and auditing needs |
responseChatItemId | Associate with API call return items | Locate specific return content for failed frontend API calls, simplifying joint debugging troubleshooting |
| Log trigger rule | Trigger by error status code | Focus on frontend online fault scenarios, reducing invalid log storage |
Three Common Misconfiguration Issues
- Symptom: When calling the FastGPT API, the
chatIdparameter is passed, but it is not recorded in the backend audit logs. Cause: The session identifier binding logic between the frontend SDK and FastGPT is not configured, resulting in thechatIdnot being synchronized to the log collection pipeline. - If backend records change after a visitor deletes a local conversation, distinguish browser sessions, platform conversation records, and team operation logs. Compare interface requests and stored records for the same conversation identifier. Do not assume that these records are bound together or that a cascading-deletion mechanism exists.
- If a record-deletion operation has no response after Docker Compose deployment, inspect the deletion request, response, authorization, and backend logs for the deployed version. Do not assume that conversation records reside in a separate audit-log directory, or replace controlled deletion with a guessed generic cleanup script.
How to Verify Proper Configuration
- Initiate a frontend page session call, view the FastGPT backend audit logs, and confirm that the
chatIdfield is correctly recorded. - Trigger a frontend error, check whether the audit logs include the full error stack and browser information corresponding to the
detailfield. - Simulate a non-logged-in user session, delete the frontend conversation, and verify that the backend audit logs are not deleted synchronously.
- In an isolated environment, use purpose-created test records to verify a deletion operation supported by the current version. Compare authorization, responses, and actual record changes, and check any operation evidence that must be retained separately. Do not run a guessed cleanup script against production records.
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.