What the data for this category looks like
Data sources include medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Update frequency is monthly. Daily processed documents include supervision notices, inspection work drafts, suspicion lists, interview records, handling decision letters, and rectification reports. Each document links to fields such as supervision subject, suspicion type, and involved business scenario. There is no unified fixed unit. Corresponding business identifiers must be matched based on document type.
What constraints these characteristics impose on the conversation logs and auditing link
Monthly updated data sources require log retention periods to be no shorter than the monthly supervision audit work cycle. This ensures full coverage of audit traceability needs. Multi-source data and multi-type business documents must have their corresponding source identifiers and document types marked in logs. This enables associated verification. Since fund supervision accountability is involved, logs must support quick retrieval by core fields such as supervision subject and suspicion type. Every conversation’s associated data retrieval, rule application, and document generation nodes must be fully retained. This prevents gaps in audit traceability.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Conversation log storage period | 35–40 days | Matches the monthly supervision audit work cycle to cover audit requirements for the full audit process |
| Single log export count | Top 20 entries | Controls the volume of log data exported per batch to adapt to daily review scenarios |
| Log storage upper limit | 1000 MB | Matches the log data scale generated by monthly supervision audits to avoid storage overflow |
| Log retrieval timeout | 60 seconds | Prevents system freezes caused by long wait times |
chatId log tracking | Enabled | Links the full process records of a single supervision audit conversation to facilitate log retrieval by audit task |
responseChatItemId associated node | Enabled | Binds the supervision data retrieval and rule application nodes corresponding to each reply in the conversation |
detail log detail switch | Full record enabled | Fully retains details required for audit such as the scope of medical insurance settlement data called and rule base entries in the conversation |
Three common configuration mistakes
- Phenomenon: Only title content is displayed in conversation logs generated via API calls, with no specific supervision audit conversation details. Cause: The
detailfull log detail configuration is not enabled, and only basic session metadata is returned. - Phenomenon: Unable to retrieve corresponding conversation logs using supervision number. Cause: The supervision number is not configured as a log retrieval field, and the logs are not associated with the corresponding business identifier.
- Phenomenon: Conversation log retention period is insufficient, making historical data untraceable after the monthly supervision audit is completed. Cause: The conversation log storage period is set shorter than the monthly supervision audit work cycle, failing to match business audit requirements.
How to confirm configurations are correct
- Initiate a simulated supervision audit conversation. Check whether logs contain complete conversation content, called data source identifiers, and rule entries.
- Configure the supervision number as a retrieval keyword. Verify that full logs for the corresponding conversation can be quickly located.
- Check whether the log storage period setting matches the monthly supervision audit work cycle. Ensure coverage of the full audit cycle.
- Call the
chatIdparameter to query logs for a specified session. Confirm that complete session node records can be obtained.
The values provided here 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 2026-09-28.