What the Data for This Category Looks Like
Data sources for education service investment research knowledge bases include official education policy documents, subject teaching and research outlines, institutional enrollment announcements, vocational training standards, student learning status statistical reports, and other sources. Update frequencies vary by type: policy documents are updated quarterly or annually, teaching and research materials are adjusted per semester, and learning status reports are generated monthly. Document structures cover long text interpretations, structured tables, and reports with quantitative indicators. Fields include policy document numbers, issuing units, effective dates, course credits, training headcount, tuition fees, and others. Units involve credits, headcount, ten thousand yuan, and more.
What Constraints These Characteristics Impose on Conversation Logging and Auditing
Multi-source, heterogeneous data structures require logs to fully record recalled knowledge sources, field values, and associated context, to avoid inability to trace knowledge citation logic during audits. Differentiated update frequencies require audit logs to synchronously record knowledge base version change nodes, to ensure knowledge used in conversations matches the currently active version. Multi-field quantitative indicators require logs to separately mark specific called values, to prevent audit deviations caused by field confusion. Segmented recall of long text documents requires recording context association for each segment, to avoid audit errors caused by taking content out of context.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 180 days | Education service audit compliance requirements usually require retaining conversation logs for more than half a year, to meet industry regulatory needs |
AUDIT_LOG_FIELDS | ["user_input", "retrieved_docs", "ai_response", "operation_time", "user_id"] | Covers core audit dimensions including user queries, recalled knowledge, AI responses, operation time, and user identity, to meet compliance traceability requirements |
MAX_CONVERSATION_HISTORY | 20 turns | Education investment research conversations usually revolve around a single scenario; excessive history increases log storage and audit complexity |
MONGO_SLOW_QUERY_THRESHOLD | 500 milliseconds | Education knowledge bases contain a large number of long-text teaching and research materials; frequent recall triggers slow queries, and this threshold accurately identifies abnormal operations |
CONVERSATION_ISOLATION_ENABLED | Enabled | Education services involve privacy data of different students and institutions; isolating sessions prevents cross-user data leaks |
SSE_CONNECTION_TIMEOUT | 600 seconds | Conversation responses after parsing long documents may take a long time; avoiding timeout disconnections ensures logs are fully written to the database |
The parameter values provided on this page are common recommended starting points for determining configurations. Actual values are affected by material format, 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 Configuration Mistakes
- Phenomenon: After deploying version 4.8.21 via Docker, when uploading a knowledge base for parsing, the log continuously reports
slow operation xxxxmsand MongoDB connection failure. Cause: TheMONGO_SLOW_QUERY_THRESHOLDwas not adjusted to adapt to large-document recall queries for education services, and the MongoDB connection pool configuration was insufficient to support high-frequency log writes. - Phenomenon: After generating a publicly accessible link, when the client closes the SSE connection, chat records are not saved to the database. Cause: The
SSE_CONNECTION_TIMEOUTwas not set to a sufficient duration, and the log forced synchronization mechanism was not enabled, resulting in incomplete log writing when the connection was interrupted. - Phenomenon: Chat records of different users are not isolated, and cross-user data visibility occurs. Cause:
CONVERSATION_ISOLATION_ENABLEDwas not enabled, or the user identity identifier was not bound to the session context during configuration.
How to Confirm Configuration Is Complete
- Access the system log management page to verify that the audit log function is enabled, and that the retention days match the
LOG_RETENTION_DAYSconfiguration. - Use two distinct test accounts to initiate conversations, then check the session list to confirm that sessions from the two accounts are mutually invisible.
- Manually trigger a query operation that includes long-document recall, then review the log to confirm that the operation’s duration and related fields are recorded.
- Close the SSE connection, then check the corresponding session table in the database to confirm that all configured audit fields including user queries, recalled knowledge, and AI responses have been fully saved.
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.