What This Category's Data Looks Like
White goods profit margin and market data sources include channel settlement data publicly released by home appliance industry associations, terminal retail data disclosed by brands, and raw material price linkage data from commodity trading platforms. Daily updates include full-category market information from the previous day. Documentation is organized by sub-categories such as freezers, drum washing machines, split air conditioners, and others. Each entry includes product SKU code, channel settlement unit price, terminal retail unit price, and unit profit amount. Unit price is measured in yuan per unit. Unit profit amount is measured in yuan.
Constraints on Deployment and Upgrade Posed by These Characteristics
Multiple scattered data sources require connecting multiple external interfaces during deployment. Configure reasonable synchronization timeout parameters to avoid task interruptions. The daily update rhythm requires scheduled tasks to match the daily data production cycle. During upgrades, compatible with newly added sub-category field mapping rules. Use single SKU as the unit for vector chunking per the SKU-based documentation structure, to avoid mixing cross-category data. Significant differences exist in unit price ranges across categories. Configure normalization rules to balance data weights. Update normalization parameters synchronously during upgrades to adapt to the price ranges of new categories.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DATA_SYNC_TIMEOUT | 600 seconds | Three data sources (home appliance industry association, e-commerce platform, commodity platform) are connected, and single synchronization takes a long time. 600 seconds covers most normal synchronization scenarios |
CRON_SCHEDULE | 0 2 * * * | Trigger synchronization at 2 AM daily. External data source interfaces have lower load at this time, which reduces the probability of synchronization failures |
VECTOR_CHUNK_SIZE | 800–1200 characters | Complete information for a single SKU includes model, unit price, profit and other fields. This chunk length can fully accommodate the core data of a single SKU |
NORMALIZATION_RANGE | Calibrated based on actual measurements | Unit price ranges vary greatly across white goods categories. Adjust the normalization range according to the actual accessed category scope |
PROXY_AUTH_ENABLE | Enabled with username and password configured | Internal network deployment scenarios require accessing external data sources through authenticated HTTP proxies to avoid failure to obtain market data |
PARSE_FIELD_STRICT_MODE | Disabled | New SKUs may carry temporary additional fields. Disabling strict mode can support dynamic fields and avoid parsing failures |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected 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 Mistakes
- Symptom: Workflow execution throws error
Cannot read properties of undefined (reading 'incl'). Cause: Dynamic field mapping is not configured, and new SKU additional fields are not recognized, leading to reading undefined properties during parsing. - Symptom: Data synchronization tasks continue to time out. Cause:
DATA_SYNC_TIMEOUTis not configured to a reasonable duration, or the internal network proxy username and password are not correctly configured, making it impossible to normally access external data source interfaces. - Symptom: Cross-category data weight imbalance in vector recall results. Cause:
NORMALIZATION_RANGEis not adjusted according to the accessed white goods categories, leading to excessive deviation in data weights between high-price and low-price categories.
How to Confirm Proper Configuration
- Execute a manual data synchronization task, check the synchronization logs, confirm there are no data source connection failure prompts, and adjust relevant configurations based on log prompts.
- View chunked data in the vector database, confirm that information for each SKU is fully chunked, with no cross-category mixing or information truncation.
- Trigger a workflow run, check if the returned market data includes the latest daily fields, with no missing or formatting errors.
- Test field parsing for new SKUs, confirm that dynamic fields can be properly recognized and included in the data reporting content.
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.