What the data for this category looks like
White goods investment research data sources include industry association public reports, official brand quarterly financial reports, offline store sales ledgers, e-commerce platform user reviews, patent databases, and more. Update cadences vary significantly: financial reports are updated quarterly or annually, store sales data weekly, user comments in real time, and patent application information updated irregularly. Document structures cover structured SKU parameter tables (with fields including model, energy efficiency rating, selling price unit yuan, shipment volume unit ten thousand units, etc.), semi-structured new product announcements, and unstructured industry research reports and user reviews.
What constraints these characteristics impose on conversation logging and auditing
The high proportion of structured data requires conversation logs to accurately record the field dimensions of user queries and specific SKU information. Audits must verify the field matching between query results and original data. Data sources with varied update cadences need to be linked to knowledge base version numbers to ensure audit trails can trace the state of the knowledge base at the time of a query. The large number of SKUs and dispersed query dimensions require the logging system to support data isolation by team and user ID, to avoid cross-scenario data mixing. Queries for unstructured review data must record keywords and source links, to facilitate tracing original content during audits.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
ENABLE_CONVERSATION_AUDIT | true | Investment research scenarios require full-link traceability to meet industry compliance requirements |
USER_ISOLATION_ENABLE | true | Adapt to multi-team investment research scenario needs, isolate conversation logs and query data across different users |
LOG_RETENTION_DAYS | 180 days | Meets compliance retention cycle requirements for the financial investment research sector |
API_CHAT_HISTORY_SCOPE | app + user_id | Prevent full-data overload under a single application, support precise query of historical records by application and user |
SSE_CONNECTION_TIMEOUT | 600 seconds | Adapt to scenarios with large volumes of white goods research reports, prevent log loss during long conversations due to timeout |
CONVERSATION_LOG_INCLUDE_FIELDS | ["query_keywords", "knowledge_version", "sku_info"] | Record core traceability fields for investment research scenarios, facilitating matching of original data and knowledge base versions during audits |
The parameter values provided on this page are common recommended starting points for configuration. 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 against your own samples before finalizing.
Three common misconfigurations
- Issue: Conversation logs do not display the knowledge base version number, making it impossible to confirm the state of the knowledge base at the time of a query during audits. Root cause: The
CONVERSATION_LOG_INCLUDE_FIELDSconfiguration does not include the knowledge_version field, leading to missing core traceability information. - Issue: Some conversation interaction content is not written to the database after the client closes the SSE connection. Root cause: No reasonable
SSE_CONNECTION_TIMEOUTduration is set, or interrupt request log persistence configuration is not enabled, resulting in uncompleted requests not having their logs retained. - Issue: Conversation logs from different investment research teams are displayed mixed together, making it impossible to isolate views by team. Root cause: The
USER_ISOLATION_ENABLEconfiguration is not enabled, and log storage dimensions are not divided by user ID, leading to data mixing.
How to confirm the configuration is correct
- Navigate to the system settings page, check the switch status of
ENABLE_CONVERSATION_AUDITto confirm it is enabled. - Initiate an investment research conversation targeting a specific white goods SKU, query parameters and market data, then view the conversation log details to confirm that core fields including querykeywords, knowledgeversion and sku_info are included.
- Switch to different user IDs to initiate the same query, confirm that logs are isolated by user ID, and that cross-user viewing of another party’s conversation records is not possible.
- Simulate an SSE connection interruption scenario, check whether all interaction content of this conversation is fully recorded in the database.
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.