What the data for this category looks like
Jewelry research and knowledge base data comes from brand official product pages, industry material standard documents, offline quality inspection reports, and cross-border e-commerce platform SKU profiles. Update schedules align with new product seasons. Regular SKUs are updated monthly, and full synchronization is completed during the season when new products launch. Each record corresponds to a single SKU. The document structure includes three modules: basic attributes, process parameters, and compliance information. Fields include:
- Material (string type)
- Weight (unit: gram)
- Design theme (string type)
- Compliance ID (string type)
- Recommended retail price (unit: yuan)
- Launch date (date type)
Constraints for Database and Operations Workflows
Multi-source data access requires configuring cross-source data validation rules. This prevents dirty data caused by inconsistent field naming across sources. Layered update schedules require incremental synchronization tasks. These tasks distinguish monthly updates for regular SKUs from full synchronizations during new product seasons, balancing database load and data timeliness. Fields with physical units require unit validation rules. This ensures consistent unit formatting for weight, retail price, and similar fields. Compliance IDs used as unique identifiers require database primary key and index configurations adapted to unique constraints. Data change logs must also be retained to meet traceability requirements for research scenarios. Additionally, parsing requirements for quality inspection report attachments require parsing parameters compatible with multiple document formats.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vectorSearchLimit | 5-10 entries | Jewelry SKU information has moderate density per entry. Excessive recall results increase API response latency, while insufficient recall fails to cover associated information required for research scenarios. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Jewelry-related quality inspection report PDFs often contain numerous parameter tables and high-resolution images, leading to longer parsing times than generic documents. |
DB_INCREMENT_SYNC_INTERVAL | 86400 seconds | Regular SKUs are updated monthly. Daily incremental synchronization balances database load and data timeliness. |
DATA_VALIDATION_RULES | Calibrated based on actual testing | Jewelry fields include unit-attached attributes such as weight and retail price. Custom validation rules are needed to match unit formatting requirements. |
MAX_CONNECTION_POOL_SIZE | 20-30 | Concurrent queries in research scenarios are mostly used for team collaboration. An overly large connection pool can lead to excessive database load. |
MONGO_VERSION_COMPAT | 4.4.29 | Some low-specification servers do not support AVX instruction sets. Compatibility with older MongoDB images is required to avoid startup failures. |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: An
Illegal instructionerror is output after starting the MongoDB container, and the service fails to start. Cause: The server CPU does not support AVX instruction sets, and an official MongoDB 5 or later image was used. - Symptom: The API response time does not shorten significantly when the same jewelry research query is submitted repeatedly. Cause: No query caching strategy was configured, so each request directly calls the vector database and relational database.
- Symptom: A large number of
compliance_idfields are empty in batch-imported jewelry SKU data. Cause: No cross-data source field mapping rules were configured, and external "quality inspection report numbers" were not correctly mapped to the corresponding database fields.
How to Verify Successful Configuration
- A MongoDB image compatibility test is run on a low-specification server, confirming that the specified version image starts normally.
- Two identical jewelry research queries are submitted, and the logs of the two requests are compared to confirm that the vector database call is not triggered for the second query.
- A batch data import task is run, and the import logs are checked for field validation failure alerts.
- The database connection pool monitoring dashboard is viewed, confirming that the current active connection count does not exceed the configured connection pool upper limit.
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.