What data for this category looks like
Yield-related data for black home appliances is sourced from terminal transaction data from retail monitoring platforms and shipping systems of brand operators, and is structured and organized by SKU. The data update cycle completes generation and release of the full dataset from the previous day every early morning. Supported document formats include two standard types: CSV and JSON. Fields include SKU code, product model, terminal retail price, channel shipping volume, and regional sales identifier. The retail price unit is Chinese Yuan, shipping volume unit is units, and the regional identifier is a categorical field.
Constraints on tool calling and plugins from these characteristics
Since the data comes from two independent systems: retail monitoring and brand shipping, cross-source data alignment logic must be configured for tool calling, using SKU code as the primary key to merge fields from different sources. The full daily dataset released in the early morning requires the workflow to be scheduled to trigger after data release is complete, to avoid pulling incomplete datasets. There are many structured data entries per SKU, so pagination parameters must be set for batch calls to limit the number of entries pulled per request, preventing interface rate limiting or response timeouts. Additionally, black home appliance model naming rules are complex, so the plugin must be configured with precise text matching rules to map product names to corresponding SKUs, avoiding data misalignment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
workflow_trigger_cron | 0 0 3 * * * | Matches the daily update schedule where black home appliance data is finalized before 3 AM, to avoid pulling incomplete datasets |
api_request_timeout | 600 seconds | Cross-source data pulling from multiple systems requires longer processing time, preventing request interruption from mid-request timeout |
batch_fetch_size | 100 items per request | Limits the number of entries pulled per batch, mitigating interface rate limiting risks and reducing workflow parsing pressure |
sku_matching_threshold | 0.85–0.95 | Black home appliance model naming rules are complex, requiring high text matching precision to avoid SKU misalignment |
mysql_connection_config | Connection pool parameters calibrated via actual testing | Database performance varies significantly across deployment environments, so connection parameters must be adjusted based on actual link conditions |
workflow_input_schema | Required fields including SKU code and data date | Aligns with the dimensions of daily updated datasets, preventing confusion between datasets from different dates |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- A
400 Bad Requesterror is returned when calling a workflow to upload a file. The cause is that the request body is not encapsulated in the multipart/form-data format required by FastGPT, and file binary data is transmitted using the application/json format instead. - An error prompt "Corresponding knowledge base not found" is returned when passing knowledge base parameters via API. The cause is that only the global knowledge base variable is configured, but the knowledge base ID parameter is not included in the request body, causing the workflow to fail to read the corresponding knowledge base resource.
- No SQL query results are returned after configuring
mysql_connection_config. The cause is that SQL execution permission is not enabled in the tool configuration, or the configured database account lacks query permission for the corresponding data table.
How to confirm configuration is complete
- Manually trigger a workflow, view the tool calling logs, and confirm that the fields returned by the data source fully match the configured
response_parse_schema. - Call the test API, pass preset SKU codes and data dates, and confirm that the calculation results returned by the workflow match the expected field structure.
- Click the test connection button for the database connection configuration, confirm that the "Connection successful" prompt is returned, and that preset SQL query statements can be executed normally.
- View the execution records of the scheduled workflow, confirm that the execution times of two consecutive working days are both after data update completion, and that there are no timeout or parameter error logs.
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.