What the data for this category looks like
White goods investment research data sources include public datasets from the China Household Electrical Appliances Association, brand quarterly and annual disclosure documents, offline store data from third-party retail monitoring institutions, public sales rankings from e-commerce platforms, and raw material quotation data from commodity trading platforms. Update cycles vary across data sources. Brand financial reports are updated quarterly. Offline retail monitoring data is updated weekly. Raw material quotations are updated daily. Industry analysis reports are released irregularly.
Document structures include structured SKU parameter tables and sales data sheets, semi-structured financial report chapters and competitor analysis snippets, and unstructured in-depth industry reports. Fields include product SKU number, product model, launch date, energy efficiency rating, recommended retail price, monthly shipment volume, upstream raw material procurement unit price, and more. Units include yuan, ten thousand units, yuan/kilogram, and others.
What constraints do these characteristics impose on deployment and upgrade
Multi-source heterogeneous data structures require configuring differentiated parsing rules for different document types during deployment. Enable field extraction for structured tables. Adjust segmentation thresholds for unstructured reports.
Data sources with different update frequencies require configuring type-differentiated incremental synchronization strategies during upgrade. This avoids excessive resource usage from full synchronization.
Diverse unit systems require configuring unified unit mapping rules during deployment. This prevents unit confusion during retrieval.
A large number of professional fields require regularly updating the field extraction model during upgrade. This adapts to newly released white goods product parameters.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | White goods investment research data includes long documents such as industry analysis reports and structured tables. The timeout period must cover the complete parsing process |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Some brand financial reports and industry reports have large single-file sizes. This setting must adapt to large-file upload scenarios |
maxContext | 800–1200 characters | White goods investment research documents often contain professional parameters and long sentences. Adjusting the context length preserves complete semantic units |
Recall count | Top 8–12 entries | Investment research data requires coverage of multi-dimensional information including sales, raw materials, and competitors. Too many recalls increase inference load. Too few result in incomplete information |
Incremental sync trigger cycle | Configured per data source type | Different data sources have different update rhythms. Synchronization cycles for daily, weekly, and quarterly updates must be adapted separately |
UNIFIED_FIELD_UNIT | Calibrated via actual testing | White goods data includes multiple units. Unified unit mapping rules must be configured for specific fields |
The parameter values provided on this page are common recommended starting points for determining configurations. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: Model API calls return a 400 error with a prompt that the parameter format does not meet requirements. Cause: The valid value range for
MODEL_API_PARAMSwas not configured. Long text requests for white goods investment research exceed the model's default parameter limits. This issue was not automatically adapted for long text scenarios in version V4.9.7. - Symptom: When uploading white goods SKU tables in batches via API, the backend service does not respond and the process hangs. Cause: The concurrency parameters for
PARSE_STRUCTURED_TABLEwere not adjusted. The number of batch-uploaded tables exceeds the concurrency limit, leading to resource exhaustion. - Symptom: After deploying with docker-compose, workflow and knowledge base configuration items become blank after a period of time, but the API can be called normally. Cause: Persistent storage volumes were not mounted. Configuration files are not retained after container restart, and a large number of knowledge base files are not synchronized to the persistent directory.
How to Confirm Configurations Are Correct
- Upload the largest single white goods investment research document, and check whether the parsing result completely extracts preset fields such as SKU and price.
- Configure an incremental synchronization task, wait for the update cycle of the corresponding data source, and check whether new investment research data is automatically pulled.
- Call a test interface, and check whether the data fields in the returned results use a unified unit system.
- Restart the deployment container, and check whether the knowledge base and workflow configurations are completely retained without loss.
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.