What the data for this category looks like
White goods data sources include brand-side manufacturing systems, offline retail store sales ledgers, online e-commerce platform transaction data, and after-sales installation and operation logs. Offline retail data updates daily based on natural calendar days. Online e-commerce transaction data updates hourly. After-sales operation data is aggregated weekly. A single data record includes SKU code, product model, sales region, transaction unit price, number of installation services, and collection timestamp. The unit of transaction unit price is yuan per unit. The unit of number of installation services is times per hundred units.
Constraints on Dialogue Logs and Auditing from These Characteristics
The call chain for multi-source data is lengthy. Auditing requires verifying each data source’s call permissions and timestamp matching individually. Different channels have varying data update frequencies. Dialogue logs must forcibly collect the original timestamp of each data source to prevent incorrect association of cross-channel data across time windows. Core fields include SKU code and number of installation services. The auditing process must verify field formats and value ranges to ensure input data complies with category specifications. Data update frequency is high. Dialogue history retention rules must match the update cycles of each data source to prevent expired retained data from negatively impacting auditing accuracy.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
logRetentionDays | 7–30 days | Matches the update rhythm of white goods offline data (daily updates) and online data (hourly updates), preventing expired retained data from affecting auditing accuracy |
auditFieldWhiteList | SKU Code, Transaction Unit Price, Installation Service Count, Collection Timestamp | Covers the core data fields of this category, ensuring auditing only validates legitimate parameters and filters irrelevant input |
dataSourceCallTimeout | 600 seconds | Adapts to the length of multi-source data call chains, preventing dialogue interruptions due to data source response delays, while controlling the waiting duration for audit backtracking |
fieldFormatCheckSwitch | Enabled | Verifies SKU code format and transaction unit price value range, preventing invalid fields from entering dialogue logs and reducing audit risks |
historySaveStrategy | Group by Session ID + Collection Timestamp | Matches the update frequencies of different channel data, ensuring that historical data within the same session uses data sources from the same time window, preventing data confusion |
exportLogSizeLimit | Calibrate by Business Scale | Adapts to the data volume scale of this category, preventing export logs from being too large and causing transmission failures, while meeting the basic needs of audit backtracking |
The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Configuration Mistakes
- Phenomenon: After multiple visits with the same session ID, the historical record of the first model call only shows 1 round of context, and subsequent calls cannot reuse previous dialogue information. Cause:
historySaveStrategyis not configured to group by session ID uniformly, resulting in historical records from different model calls being stored independently and no complete session link being formed. - Phenomenon: A
413 Request Entity Too Largeerror is returned when exporting dialogue logs, and the export operation cannot be completed. Cause: TheexportLogSizeLimitconfiguration is not adjusted, and the default configuration cannot handle the log data volume with multiple fields for this category. - Phenomenon:
500 Internal Server Errorstatus codes indicating database connection failure frequently appear in audit logs, and supply chain or retail data cannot be obtained normally. Cause:dataSourceCallTimeoutis not configured to adapt to multi-source data chains, or data source connection parameters do not include the port number and permission verification fields exclusive to white goods systems.
How to Verify That the Configuration Is Correct
- Enter the FastGPT log management page, filter the target session ID, and check whether the context rounds of the historical record comply with the
historySaveStrategyrules configured. - Manually trigger a cross-channel data call, check whether all fields in the configured
auditFieldWhiteListare included in the log, and whether the field formats comply with category specifications. - Try to export logs for a small number of sessions, confirm there are no errors, then adjust
exportLogSizeLimitto a value matching business requirements. - Disconnect the connection of a specified data source, check whether timeout information related to
dataSourceCallTimeoutis correctly recorded in the log, and verify that the configuration takes effect.
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.