Conversation Logs and Auditing for Education Service Yield

What the data for this category looks like

Education service scenario yield rate and market data primarily comes from public APIs of licensed financial data service providers and standardized reports released after daily market close. Data is updated once daily, with synchronization completed within 1 hour after market close on trading days. Each data entry includes fields such as product identifier, affiliated category, daily annualized yield rate range, net value change range, corresponding underlying market index, update timestamp, and others. Field units follow general financial industry specifications: yield rate fields use percentage as the unit, net value fields use Chinese Yuan as the unit, and market index fields use the official code of the corresponding underlying asset.

Constraints Imposed by Data Characteristics on Conversation Logs and Auditing

The daily update characteristics of the data source require that conversation logs be archived by trading day, to facilitate backtracking of market data-related records for specific periods during audit cycles. The structured nature of multi-field data requires the auditing process to verify the completeness and format compliance of fields such as yield rate and net value associated with each conversation, to avoid audit failure caused by missing fields. The entire third-party data source call chain must be recorded, including interface request ID, response status code, and data synchronization duration, for traceability when troubleshooting data anomalies. Conversations in education service scenarios may involve wealth management knowledge explanations and market interpretations, so user questions, AI responses, and the version number of corresponding market data must be bound together, to ensure that reference materials at the time of the conversation can be restored during audits. Additionally, due to the high frequency of data updates, outdated versions of market data must not be retained in logs, to ensure that reference information during audits matches the data source at the time of the conversation.

Configuration Settings

Configuration Item	Recommended Setting	Rationale
`enableAuditLog`	Enabled	Education service scenarios must meet compliance auditing requirements, and fully record all interaction chains and data association details
`logRetentionDays`	`180 days`	Meets audit compliance retention period requirements for finance-related scenarios, covering the scope of regular audit backtracking
`apiRequestLogEnabled`	Enabled	Full recording of call details for third-party financial data interfaces is required for traceability of data anomalies and chain issues
`LOG_LEVEL`	`DEBUG`	Ensures all levels of log information are recorded, including ERROR-level exception reports, to facilitate complete auditing
`maxContext`	`First 5 entries`	Market briefing conversations in education service scenarios typically do not involve excessive historical context; limiting the number saves storage and computing resources
`logStorageQuota`	`200 GB`	Calculated based on approximately 100 MB of new logs per day, this can meet storage needs for at least 1000 days, adapting to regular audit cycles

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

Three Common Misconfigurations

Symptom: In docker-deployed instances of version V4.9.13, model background can view response logs, but workflow conversation interface shows failure. Cause: The apiRequestLogEnabled parameter is not enabled, causing workflow nodes to fail to capture the call status of third-party data interfaces, triggering chain interruption.
Symptom: After an application conversation triggers an exception error, no corresponding record can be found in the system logs. Cause: The LOG_LEVEL is configured to WARN or higher, and ERROR-level exception details are not recorded, making it impossible to trace the error cause during audits.
Symptom: Unable to retrieve the conversation record ID of the previous AI reply, and the call node returns a null value. Cause: The enableAuditLog parameter is not enabled, and the system does not generate a unique ID field for conversation records, making it impossible to pull the corresponding identifier via the API.

How to Verify Proper Configuration

Enter the system log management interface, filter for ERROR level logs, initiate a conversation containing yield rate keywords, trigger a simulated exception, and confirm that the exception information is fully recorded.
Call the audit log API provided by the system, pass any valid conversation ID, and confirm that the returned results include conversation context, associated market data version number, and interface call chain details.
Check the docker-deployed log storage directory or cloud storage bucket, confirm that trading day archive log files named in YYYY-MM-DD format exist and have complete content.
Adjust the maxContext parameter to First 3 entries, initiate four rounds of conversation, and confirm that the system only recalls the most recent 3 historical conversation records, matching the configured expectation.

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.