Dialogue Logs and Auditing for Financial Leasing Investment

What the data for this category looks like

Financial leasing investment research data comes from multiple sources: internal lease contract archives, leased asset operation and maintenance ledgers, enterprise credit information published on the central bank’s credit reporting system, supply and demand data for lease categories released by industry associations, and publicly available equipment quotation documents from manufacturers.

Update frequencies vary across sources: lease contract archives are updated in real time after a contract is signed, leased asset operation and maintenance ledgers are updated weekly, and industry supply and demand data is updated monthly.

Documents are split into two categories: structured tables and unstructured documents. Structured documents include fields such as contract number, lessee’s unified social credit code, original value of leased asset, amount of each installment rent, and lease term duration. Unstructured documents include lease project due diligence reports and industry trend analysis, with fields including project number, report release date, and core conclusions.

Field unit specifications: original value is measured in ten thousand yuan, rent amount is measured in yuan, and lease term duration is measured in months.

What constraints these characteristics impose on the dialogue logs and auditing workflow

Financial leasing investment research data has a high proportion of structured data and high field complexity. This requires dialogue logs to fully record the field names and matching results of each call match, to avoid field mapping errors.

There are significant differences in update frequencies across different data sources. The auditing link must associate log timestamps with the update times of corresponding data sources, to ensure that valid data from when the project was initiated is used during backtracking.

Unstructured documents have long lengths. Logs must record the start and end positions of recalled fragments, to enable quick location of audit basis.

Additionally, compliance auditing requirements in the financial leasing industry require retention of full-link operation traces. Logs must include complete information about call parameters, return results, and user-triggered actions, without omitting critical links.

How to configure the settings

Configuration Item	Recommended Value	Rationale
`LOG_RETENTION_DAYS`	`90 days`	Matches the standard retention period for compliance audits in the financial leasing industry
`AUDIT_LOG_INCLUDE_CONTEXT`	`Enabled`	Fully records dialogue context to meet full-link audit backtracking requirements
`MAX_LOG_ENTRY_SIZE`	`20480 bytes`	Adapts to the log recording needs of long documents in financial leasing, avoids truncation of critical information
`LOG_ERROR_STACK_TRACE`	`Enabled`	Fully captures stack traces of call errors, to facilitate problem troubleshooting
`EXPORT_AUDIT_LOG_FORMAT`	`CSV`	Facilitates import into compliance audit systems for standardized organization
`CONTEXT_RECALL_LOG_ENABLE`	`Enabled`	Records recalled knowledge base fragment information, supports traceability auditing of investment research data

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

Three common configuration mistakes

Symptom: Call logs return 500 Internal Server Error with the prompt "Failed to fetch dialogue data". Cause: No reasonable value configured for LOG_RETENTION_DAYS, leading to excessive storage usage by log files and subsequent read failure.
Symptom: Unable to clear specified dialogue records after deploying with Docker Compose. The interface still displays the records. Cause: No log persistence volume is mounted. Temporary storage dialogue records are not cleaned up after the container is deleted, or the docker compose down -v command to remove anonymous volumes is not executed.
Symptom: Unable to retrieve historical dialogue records after sharing the web application. Logs show the missing session_id field. Cause: The SESSION_PERSISTENCE_ENABLE configuration is not enabled, leading to no persistent storage for dialogue sessions.

How to confirm the configuration is complete

Run the docker logs fastgpt-app command. Check if the log contains the audit log initialized successfully prompt to confirm the log service started normally.
Initiate an investment research-related dialogue call. Check if the backend log contains matching field information and position records of recalled document fragments to confirm complete log recording.
Enter the log management page in system settings. Try to export audit logs for a specified time period to confirm the export format matches the configured settings.
Manually delete a test dialogue record. Check if the corresponding storage directory or database has removed the session data to confirm the cleanup logic is effective.

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.