What the data for this category looks like
This category's data primarily comes from public industry monitoring platforms, official brand retail data, and supply chain quotation systems. Terminal retail data is updated daily, while supply chain quotations are updated each workday. Each individual data document includes product SKU, launch cycle, current terminal selling price, upstream component procurement price, channel inventory quantity, and monthly shipment forecast. Fields cover identification, time, price, inventory, and forecast categories. Price fields use yuan as their unit, inventory fields use units as their unit, and there are no additional special unit fields.
What constraints do these characteristics impose during deployment and upgrade?
Multiple data sources with different update rhythms require configuring multiple data source authentication parameters during deployment, and adjusting the scheduling periods of scheduled synchronization tasks to avoid triggering rate limits from each data source via concurrent requests. A large number of SKUs and rich field categories require configuring precise field mapping rules during deployment to avoid field missing or type mismatch during parsing. High data update frequency requires adjusting the incremental synchronization trigger logic during upgrades to reduce resource usage from full synchronization. Additionally, the price fields of consumer electronics data have high precision requirements, so the storage precision parameter for floating-point fields must be configured during deployment to avoid data distortion.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Consumer electronics market daily reports have large individual document data sizes, requiring sufficient parsing time |
UPLOAD_FILE_MAX_SIZE | 500 MB | Historical market archive files for the consumer electronics category are typically large in size, requiring relaxed upload limits |
VECTOR_STORE_SHARD_COUNT | 8–12 | The consumer electronics category has a large number of SKUs; sharded storage improves retrieval concurrency |
SCHEDULER_SYNC_INTERVAL | 3000 seconds | Terminal retail data is updated daily; incremental synchronization every hour balances real-time performance and resource usage |
FLOAT_FIELD_PRECISION | 6 decimal places | Consumer electronics price fields require sufficient precision to avoid data errors |
PLUGIN_MATCH_RULE | Match by field prefix | Consumer electronics data field names follow a unified prefix, enabling quick field mapping |
The parameter values provided on this page are all 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 conduct tests on relevant samples before finalizing settings.
Three Common Mistakes
- Issue: Uploading knowledge base files fails during Docker deployment, and the interface returns the
413 Request Entity Too Largeerror. Cause: TheUPLOAD_FILE_MAX_SIZEconfiguration item was not adjusted, and the default value is too small to accommodate consumer electronics market archive files. - Issue: After upgrading to beta4 version, an error prompt
MongoServerError: The dollar ($) p(partial error message truncated) appears when configuring the data parsing plugin, causing plugin verification to fail. Cause: The currently used MongoDB version is 4.4.29, which has syntax compatibility issues with the beta4 version plugin; the version needs to be adjusted for adaptation. - Issue: After local deployment, calling the interface to create knowledge base vectors results in high server memory or disk usage at fixed daily times. Cause: Incremental synchronization logic was not configured, and full synchronization of all consumer electronics SKU data is performed daily, leading to overload of read and write pressure.
How to Confirm Proper Configuration
- Run a parsing test for a single market document, and verify that the parsed fields match the data source fields. Adjust the matching rules according to the actual field names of the data source.
- Trigger an incremental synchronization task, check the synchronization time and resource usage, and adjust the synchronization interval threshold based on real-time performance requirements.
- Upload a test archive file matching the category characteristics, confirm that the upload and parsing processes have no errors, and that the file size complies with the configured upload limit.
- Run the plugin verification script, confirm that there are no version-related error messages, and verify the compatibility between the plugin and the current deployment environment.
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.