What the data for this category looks like
Small home appliance profit margin and market data is sourced primarily from the national energy efficiency label public database, third-party home appliance retail monitoring APIs, and manufacturer supply price documents. Full daily data for all categories is updated between 2 AM and 4 AM each day. Incremental real-time price adjustment data for popular SKUs is added every 6 hours. Data is stored as structured JSON format. Each data entry includes fixed fields:
sku_id: 12-digit numeric stringproduct_name: Chinese product namepurchase_price: Floating point type, unit: yuanretail_price: Floating point type, unit: yuanhourly_energy_use: Floating point type, unit: kilowatt-hourmarket_avg_price: Floating point type, unit: yuandaily_energy_saving: Floating point type, unit: yuan
All data fields use a flat structure with no nested complex layers.
What constraints these characteristics impose on tool calling and plugin configuration
The multi-source nature, layered update schedule, and flat structured format of small home appliance data impose clear constraints on tool calling and plugin setup. Multi-source data sources require plugins to support parallel calls to multiple APIs and merge deduplicated results, to prevent data loss caused by anomalies in a single data source. The daily full update schedule paired with 6-hour incremental updates for popular SKUs requires plugins to use mixed scheduled tasks: run full pull tasks daily, and incremental pull tasks for popular SKUs every 6 hours. Single batch request SKU count must be limited to avoid interface timeouts. The flat JSON structure means plugins do not need to handle nested layers during field mapping, but field names and data types must be matched strictly. Incorrect matching will cause errors in subsequent profit calculation logic. The large total number of SKUs requires plugins to configure pagination pull parameters, to prevent excessively large data returns from single requests.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
tool_call_timeout | 300 seconds | Small home appliance data interface responses typically complete within 120 seconds. A 300-second timeout provides sufficient buffer to avoid task failure from data pull timeouts |
plugin_max_parallel | 3–5 | Small home appliance SKU counts are relatively high. Excessive parallel calls will trigger interface rate limits, while too few parallel calls will extend full data pull duration |
page_size | 50–100 entries | Excessively large per-page data volume will cause interface response timeouts. Too small a value will increase request counts. A suitable range should be determined through actual testing |
retry_times | 2 | Small home appliance data interfaces occasionally experience network fluctuations. Too many retries will extend task cycles, while too few retries will not cover temporary exceptions |
field_mapping_strict | Enabled | Small home appliance data field formats are fixed. Strict matching avoids profit calculation deviations caused by field parsing errors |
scheduled_task_cron | 0 2 * * * | Data sources complete full daily data updates in the early morning. Pulling data at this time ensures access to the latest complete dataset |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing values.
Three common configuration errors
- A 400 Bad Request error is returned by plugin calls, and the
daily_energy_savingfield returns empty. The cause is failure to strictly match data source fields per configured field mapping rules, with a typo replacingdaily_energy_savingwithenergy_savingleading to parsing failure. - Only partial SKU data is pulled by scheduled tasks, with no full update completed. The cause is failure to configure the
page_sizeparameter, or using a value exceeding the single return limit of the interface, leading to pagination logic interruption. - No valid return results are generated when calling custom Python script plugins. The cause is failure to deploy the Python script as an accessible interface service, or failure to correctly fill in the interface address and request parameters in the FastGPT plugin configuration. This corresponds to a common community issue for connecting Python code via custom plugins.
How to verify correct configuration
- Access the FastGPT plugin debug page, manually trigger a single plugin call, and check that returned data includes all configured fields with values matching public source information.
- Review scheduled task execution logs, confirm that daily full pull tasks and 6-hour incremental pull tasks execute normally with no timeout or error records.
- Simulate multiple rounds of tool calling scenarios, verify that the plugin correctly merges results when calling multiple data sources in parallel, with no duplicate or missing data.
- Disconnect one data source interface to trigger an exception scenario, verify that the plugin's retry mechanism and exception handling logic take effect, with no task interruption.
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.