What the data for this category looks like
Home goods investment research data primarily comes from monthly analysis reports of light industry manufacturing industry associations, brand dealer inventory ledgers, mainstream e-commerce platform SKU sales monitoring data, and customs import and export declarations. Data update cycles cover monthly, weekly, and daily frequencies: industry reports update monthly, sales monitoring data updates weekly, and real-time price fluctuations update daily. Document structures include fields such as SKU code, material, specification, selling price, inventory, and channel share. Units include yuan/item, item, ten thousand yuan, and other standard units. Single detailed documents often contain batch parameters for multiple SKU categories.
What constraints these characteristics impose on deployment and upgrade
Multi-source heterogeneous data sources require support for multi-format access configurations during deployment to avoid data format conflicts. Data sources with different update cycles need configured differentiated scheduled synchronization tasks to prevent duplicate synchronization or missed updates. Large batch SKU detailed document volumes require adjustment of file parsing timeout and size limits to avoid parsing interruptions. Highly refined fields require configured field mapping rules to ensure consistency of investment research data. The upgrade phase must support newly added industry-specific data parsing modules to prevent parsing exceptions for existing data sources caused by version updates.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Home goods investment research data often includes detailed tables for multiple SKUs, resulting in large single-file sizes |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Batch parsing of multiple SKUs requires longer processing time to avoid mid-parsing timeouts and interruptions |
chunkSize | 800–1200 characters | Home goods product parameter and channel analysis documents have long lengths, adapted for long-text segmentation |
similarityThreshold | 0.75–0.85 | Precise matching of parameters and market data for the same category of SKUs is required to avoid low-relevance recalls |
recallTopK | Top 8–12 entries | Investment research analysis requires a balance between comprehensiveness and accuracy, balancing returned data volume |
SYNC_CRON_EXPR | 0 0 2 * * * / 0 0 * * * | Adapts to different update cycles of monthly industry reports and weekly sales data |
The parameter values provided on this page are common starting points 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 values.
Three common mistakes
- Symptom: The
docker pullcommand returns a404 Not Founderror or pull timeout. Cause: Domestic mirror acceleration addresses are not configured. Home goods investment research scenario images include industry-specific datasets, resulting in high pull bandwidth usage. - Symptom: After upgrading to version
v4.8.14-fix, the voice broadcast function becomes unresponsive. Logs showcosyvoice module not loaded. Cause: Environment variable configuration for the voice service is not added todocker-compose.yaml. Home goods investment research scenarios often require generating voice documents for market interpretations, which depend on the newly added voice module. - Symptom: After calling the code running interface, the client cannot deserialize the returned result, and a
500 Internal Server Errorstatus code is returned. Cause: The frontend access address is not configured indocker-compose.yamlper the new version requirements, resulting in returned data format verification failure.
How to verify successful configuration
- Run
docker-compose up -d, then check container logs to confirm all dependent services start without errors. Verify that scheduled synchronization task trigger times match the configured values based on data source type. - Upload a home goods SKU detailed document, check that parsed fields match preset mapping rules, and that segmentation lengths meet configured requirements.
- Initiate a vector recall test, confirm that the number of returned recall entries matches the configured
recallTopKvalue, and that similarity matching results meet business needs. - After upgrading the version, verify that the voice broadcast function and code running interface return results can be parsed normally, with no format errors.
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.