What the data for this category looks like
Snack food profit margin and market data comes from brand offline POS cash register systems, third-party e-commerce sales backends, and dealer inventory ledgers. Data is split by SKU. Offline store data updates daily. Online channel data updates hourly. Soon-to-expire promotional data syncs in real time. Each data entry includes the following fields: SKU code, product name, sales channel, daily revenue, purchase cost, unit selling price, and remaining inventory. Revenue and cost are measured in yuan. Remaining inventory is measured in units. Selling price is measured in yuan per unit.
What constraints these characteristics impose on conversation logs and auditing
The multi-SKU, channel-separated data structure requires conversation logs to aggregate records by both SKU and sales channel dimensions. Without this aggregation, it is impossible to match the specific product scenario of a user query. The mixed update schedule of daily, hourly, and real-time updates requires audit logs to record the pull timestamp for each data entry. This allows verification of the timeliness of market trend reports. The presence of multiple numeric fields requires the audit process to verify field unit consistency. This avoids unit confusion during cross-SKU statistics. Differences between multiple data sources require clear marking of the source interface for data pulls in logs. This facilitates locating data source deviations in market trend reports.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_RETENTION_DAYS | 90 days | Aligns with standard retention cycles for retail industry compliance audits |
maxContext | 8000–12000 characters | Meets long context recording needs for multi-SKU market data |
AUDIT_FIELD_WHITELIST | ["sku_code", "sale_channel", "daily_revenue", "unit_price"] | Only retains fields required for auditing to avoid log redundancy |
DATA_FETCH_TIMEOUT | 30 seconds | Adapts to response times for parallel pulls across multiple data sources |
LOG_QUERY_SAMPLING_RATE | 100% | Retail audits require full recording of conversations and data interactions, with no sampling allowed |
HISTORY_CLEANUP_CONDITION | Triggered by the expiration date of SKUs associated with conversations | Matches the audit cycle logic for soon-to-expire snack food inventory |
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 against your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Data source interface calls return
400 do request failedorcommon:code_error.error_message.403errors. Cause: The access whitelist for the data source interface is not configured, or request parameters do not match the format requirements for snack food SKU codes. - Phenomenon: The output fields of a specified reply component are not displayed in the workflow global variable history. Cause: The fields output by the component are not configured in
AUDIT_FIELD_WHITELIST, resulting in the content not being included in logs. - Phenomenon: Historical records generated in debug preview conversations cannot be cleaned up per specified conditions. Cause: Cleanup is incorrectly triggered at the full application level, instead of matching the SKU association rule configured in
HISTORY_CLEANUP_CONDITION.
How to confirm correct configuration
- Check the
LOG_RETENTION_DAYSconfiguration item to confirm the retention period meets internal audit rule requirements. - Initiate a snack food profit margin query conversation, and verify that all required fields configured in
AUDIT_FIELD_WHITELISTare included in the logs. - Trigger a data source pull operation, and confirm that the interface response time does not exceed the
DATA_FETCH_TIMEOUTconfigured value. - Manually trigger historical record cleanup, and confirm that only conversation logs associated with specified SKUs are cleaned up, without clearing full application history.
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.