Conversation Logs and Auditing for Commercial Real Estate

What this category of data looks like

Commercial real estate yield data is a core indicator for real estate operations in financial and wealth management scenarios. The system sources data from project rent collection ledgers, vacancy rate statistics systems, and third-party real estate market APIs. The system updates data daily to sync same-day lease performance and venue usage status. It completes full-cycle yield calculations monthly. Each data entry includes these fields: project unique identifier, project name, same-day accrued rent, same-day collected rent, vacant building area, and cumulative revenue over the accounting cycle. It uses standardized units: monetary values use yuan, area values use square meters, and cycle values use natural days or natural months.

Constraints on conversation logs and auditing

Logs must record exact timestamps for data source calls to match the daily update cadence of commercial real estate yield data. This ensures query results align with actual update times. Logs must fully record specific fields hit by a single query and their returned values, due to the structured, multi-field format of the data. This supports tracing data sources during audits. Logs must link requests to their corresponding project IDs, because each entry includes a project unique identifier. This enables audit tracing by project. Logs must record API response status codes, because the system depends on third-party market APIs. This helps troubleshoot data acquisition failures. Logs must retain full request parameters and processing time, to support high-frequency repeated queries in operational scenarios. This supports compliant audit workflows.

Configuration settings

For FastGPT v4.9.7 and later, the following configuration items work for this scenario:

Configuration Item	Recommended Setting	Rationale
`MAX_LOG_RETENTION_DAYS`	30 days	Commercial real estate operations require compliance audits to cover full monthly accounting cycles. 30 days matches standard audit cycles
`LOG_SAVE_MAX_COUNT`	12000 entries per day	Daily query volume per project typically falls in the hundreds range. 12000 entries supports concurrent query needs across multiple projects
`AUDIT_FIELD_WHITELIST`	`project_id, actual daily rent, vacant building area`	Only retain core fields required for audits to reduce log storage redundancy
`API_REQUEST_TIMEOUT`	60 seconds	Third-party real estate market APIs typically respond in 30-50 seconds. 60 seconds covers normal invocation scenarios
`ENABLE_DB_PERSIST_LOG`	Enabled	Persist conversation logs to the database. This supports audit searches by project ID and time range
`LOG_CLEANUP_CRON`	`0 0 2 * * *`	Run expired log cleanup daily at 2 AM to avoid overloading database storage resources

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by data format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Symptom: The aiproxy_pg container fails to start when running docker-compose to launch the service. Logs return a database connection failed error. Cause: The database persistence directory was not mounted in advance. When the container restarts, log data overwrites initial configuration, causing database initialization failure.
Symptom: After a large model interface call fails, corresponding audit logs for the request cannot be retrieved. Cause: The ENABLE_DB_PERSIST_LOG configuration was not enabled. Logs are only output to temporary console cache, and logs are lost after container restart.
Symptom: Specific business fields hit by queries do not appear in conversation logs. Auditors cannot trace data sources. Cause: AUDIT_FIELD_WHITELIST was misconfigured, excluding core business fields from the audit scope.

How to confirm successful configuration

Run the docker logs aiproxy_pg command. Check that the database container startup logs contain no connection error alerts.
Navigate to the system configuration page. Verify that the AUDIT_FIELD_WHITELIST configuration includes core business fields required for operations.
Initiate a yield query request. Wait for the request to complete. Use the audit log module to search by project ID. Confirm that a complete record of the corresponding request can be found.
Access the system scheduled task management interface. Confirm that the log cleanup task has been configured and enabled.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.