What the data for this category looks like
The market trend and yield rate data for small home appliances mainly comes from brand-owned ERP systems, mainstream e-commerce platform sales backends, and third-party home appliance industry market trend monitoring APIs. The system synchronizes full records of the previous natural day every early morning. Each single data document includes fields such as SKU Code, Brand Name, Product Model, Current Terminal Selling Price, Purchase Cost, Average Daily Usage Duration, Unit Input Return Amount, Energy Efficiency Grade, etc. The units of selling price and purchase cost are yuan per unit, the unit of average daily usage duration is hours, and the unit of unit input return amount is yuan.
What constraints these characteristics impose on model access and configuration
Multi-source data sources require configuration of multiple sets of authentication parameters to ensure correct access permissions for different data sources. The large volume of daily full update data requires reasonable synchronization interval and timeout threshold settings to avoid synchronization task interruptions. The high number of fields including numeric indicators requires configuration of field filtering rules to retain only core analysis fields and reduce vector database storage pressure. The unified units of numeric fields with obvious magnitude differences require configuration of normalization processing parameters to avoid model preference for high-magnitude fields.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
dataset_sync_interval | 86400 seconds | Matches the daily update rhythm of small home appliance market data, avoids repeated synchronization or delayed data acquisition |
vector_store_field_whitelist | ["SKU Code", "Brand Name", "Current Terminal Selling Price", "Unit Input Return Amount"] | Retains only core analysis fields to reduce vector database storage and calculation pressure |
PARSE_DATA_TIMEOUT_SECONDS | 600 seconds | Full small home appliance data pulling and parsing takes a long time, avoids synchronization task interruptions due to timeout |
embedding_model_batch_size | 32 | Balances batch processing performance and memory usage, adapts to the single-batch data volume of small home appliances |
rag_recall_top_k | Top 8 entries | The volume of single small home appliance SKU data is moderate, and 8 recalls can cover core market trend and yield rate information |
tool_call_enabled | Enabled | Supports the model to call external market trend APIs to supplement real-time selling price and sales data |
The parameter values provided on this page are all common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to conduct tests on your own samples before finalizing the configuration.
Three Common Configuration Errors
- A 401 status code is returned when calling the external market trend API. This occurs because the
one_api_base_urlandone_api_keyparameters are not configured correctly, leading to authentication failure. - The vector index task stays in the "In Progress" state for a long time with no progress. This occurs because the
vector_store_field_whitelistfiltering redundant fields is not configured, resulting in excessive data volume written to the vector database and excessively long processing time. - The model cannot automatically call the real-time market trend tool. This occurs because the
tool_call_enabledswitch is not enabled, or tool call rules are not clearly specified in the system prompt.
How to Confirm the Configuration is Complete
- View the dataset synchronization log to confirm that the synchronization task executes according to the set cycle, and the synchronized data fields match the configured whitelist.
- Initiate a market trend query request for a single SKU, and verify that the returned result fields exactly match the configured embedded fields.
- Trigger a tool call test to confirm that the model can call the specified market trend API according to the configuration and return real-time data.
- View the vector index task list to confirm that no abnormal errors occur after the index task is completed, and the index progress meets expectations.
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.