What Data for This Category Looks Like
Data sources for other comprehensive yield rates and daily market reports include public market aggregation APIs, internal institutional accounting ledgers, and third-party compliant data sources. The daily dataset is generated at a fixed time each day. Each record in the dataset contains the following fields: asset unique identifier, asset category tag, daily market benchmark value, daily price fluctuation range, and daily trading volume. For field units, benchmark values and fluctuation ranges use standard financial pricing units, while trading volume uses currency units.
What Constraints Do These Characteristics Impose on Conversation Logs and Auditing
Multiple data sources require logs to fully record data call source identifiers, to avoid audit deviations caused by cross-source data confusion. The fixed daily update rhythm requires the auditing process to verify the match between log generation time and data source update time, to prevent expired data calls from affecting audit accuracy. The multi-field structure requires logs to retain all core associated fields; missing key information such as asset identifiers and fluctuation ranges will prevent compliance checks. Cross-category identification requires logs to be associated with the asset category tag from user queries, to ensure that the audit process can trace specific category conversation interaction flows.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
conversationLogRetentionDays | 30 days | Meets the general compliance retention period for financial industry audit logs, covers full business verification cycles, and is compatible with V4.9.3 configuration logic |
apiDataCacheExpireTime | 86400 seconds | Matches the daily update rhythm of market data for the other comprehensive category, prevents calls to expired historical market data, and ensures the timeliness of market information broadcast in conversations |
logCaptureFields | ["userQuery", "aiReply", "dataSource", "quoteTime", "sessionId"] | Only captures fields required for auditing, ensures logs only retain interaction and data information relevant to compliance checks, and reduces unnecessary storage overhead |
maxAuditQueryBatch | 500 entries | Adapts to batch auditing scenario requirements, balances query efficiency and system load, and meets the basic query volume for daily audits |
auditAutoCleanThreshold | Automatically clean expired logs quarterly | Reduces resource consumption from long-term storage, meets compliance retention requirements, and prevents invalid logs from occupying system resources |
The parameter values provided on this page are common starting points for 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.
Three Common Configuration Mistakes
- Symptom: Operational data in conversation logs is empty after triggering a workflow that calls other comprehensive category market data. Cause: The
dataSourceandquoteTimefields were not included in thelogCaptureFieldsconfiguration, so core data information was not captured. - Symptom: The association between user queries and AI replies is mixed up in results returned by the
getConversationListAPI. Cause: The binding relationship betweensessionIdanduserQuerywas not retained during log capture, leading to mixed interaction data from different sessions. - Symptom: Unable to retrieve conversation logs for the other comprehensive category by specified collection. Cause: The collection identifier field for the session was not included in the
logCaptureFieldsconfiguration, making it impossible to search logs by collection dimension.
How to Verify Proper Configuration
- Initiate a test conversation that includes a query for other comprehensive category market data, and check if the conversation log fully includes the preset core fields.
- Call the
getConversationListAPI, pass the test session’ssessionId, and confirm that the returned results correctly associate user queries with AI replies. - Perform a batch query for a specified user’s historical conversation records, and confirm that the number of returned entries matches the expected business range.
- Trigger a manual audit process, and confirm that the system can properly generate and retain complete audit logs for the current session.
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.