What the data for this category looks like
This data typically comes from CRM systems, contract order databases, identity management systems, and product backends. Updates occur per order or daily. Daily associated documents include onboarding application forms, tenant configuration sheets, administrator account lists, and delivery confirmation forms. Data fields include tenant unique identifiers, customer numbers, onboarding times, permission configuration items, account statuses, and additional relevant fields. There are no fixed uniform unit requirements. Fields adjust dynamically based on the specific configuration of delivery orders.
What constraints these characteristics impose on conversation logs and auditing
Multi-source data collection requires the logging system to support cross-system association. It binds tenant identifiers to operation logs from each source. Daily or per-order update rhythms require audit log synchronization and storage cycles to match delivery archiving requirements. This prevents data loss that could impact acceptance checks. Dynamic field adjustment requires logging collection rules to support custom field mapping. This ensures full traceability of content from onboarding application forms, permission configurations, and related documents. Error consequences during permission initialization require audit logs to cover key steps such as account creation and permission distribution. This allows quick localization of issues such as customer login failures.
How to configure settings
This guide is compatible with FastGPT v4.17.0. Specific configurations are listed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId association rule | Bind tenant ID and onboarding application form number | Match the unique identifier for tenant initialization, enabling cross-document audit data association |
detail field collection scope | Include permission configuration, account creation, and Feishu Webhook call logs | Cover operational details across the entire onboarding process, meeting traceability needs for troubleshooting |
| Log retention period | 180–365 days | Match the archiving cycle of delivery confirmation forms, aligning with standard audit traceability requirements |
| Exception alert trigger threshold | More than 2 responseChatItemId missing entries within 1 second | Correspond to high-concurrency call exception scenarios, enabling timely capture of workflow call failures |
| Cross-source data synchronization interval | 5 minutes | Align with daily update rhythms, ensuring audit data timeliness |
Three common configuration errors
- Symptom: Specified reply content is not saved to session history. Reopening the session displays empty automatic replies. Cause: Full-process collection of the
detailfield is not enabled, orchatIdis not correctly bound to the unique session identifier for tenant initialization. - Symptom: Onboarding operations from different sources cannot be distinguished in MongoDB logs. Cause: No
chatIdassociation rule for customer sources and tenant IDs is configured. This results in a lack of unique distinguishing identifiers for log aggregation. - Symptom: MCP returns empty results during high-concurrency workflow call stages. Cause: Return logs for
responseChatItemIdare not collected, and no exception trigger threshold is set. This prevents timely capture of parameter errors or timeouts in concurrent scenarios.
How to verify correct configuration
- Export a complete onboarding application form. Verify that the
chatIdvalue in logs matches the unique identifier of the application form. - Manually trigger a tenant initialization operation. Check that logs include specific content such as permission configuration and account creation under the
detailfield. - Simulate 2–3 concurrent calls within 1 second. Confirm that the system generates corresponding exception alert logs.
- Review log storage configurations. Verify that old logs older than 180 days are archived or retained according to configured rules.
The values listed above are common starting points. They should be evaluated 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.