What Data for This Category Looks Like
Data related to black appliance yield rates comes from three main sources: domestic home appliance retail monitoring databases, publicly available monthly operating reports from brands, and daily sales data synced from offline supermarket POS machines. Data updates follow three schedules: offline retail POS data updates daily, brand-side revenue data updates monthly, and overall industry monitoring data updates every ten days. Each data entry includes these fields: brand name, product model, SKU code, distribution region, statistical cycle, sales volume, unit selling price, and total revenue. Corresponding units are text, text, text, text, date, units, yuan per unit, and yuan respectively.
Constraints Imposed on Form and Interaction
The multi-dimensional nature of the data and varied update frequencies create multiple constraints for the form and interaction process. First, data with different update cycles requires matching filtering logic. The form must support switching statistical cycles such as daily, ten-day, and monthly to avoid mixing data across cycles. Second, the multi-field, multi-dimensional structure requires multi-level linked filtering in the form. For example, selecting a brand automatically loads its corresponding models, and selecting a region limits the range of queryable SKUs to reduce invalid input. Additionally, differences across multiple data sources require a clear data source switching entry in the interaction. Field validity checks must also be added, such as restricting sales volume and selling price to non-negative values, to prevent invalid data from entering subsequent workflows.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
filter_condition_logic | Configure three-level linked filtering based on "brand + model + statistical cycle" | Matches the filtering needs of black appliances with multiple SKUs and multiple cycles, improving retrieval accuracy |
recall_top_k | Top 10-15 entries | Black appliance SKU volumes are high per cycle. Too many recall results increase interaction burden, while too few fail to cover core data |
chunk_size | 800-1200 characters | Black appliance data entries have many fields. Too long a segment causes semantic fragmentation, while too short a segment loses the association between SKUs and revenue information |
workflow_http_input_schema | Configure numerical checks for sales volume ≥ 0 and selling price ≥ 0 | Prevents invalid data from entering the workflow, complying with the valid range requirements for black appliance data |
knowledge_base_switch | Bind corresponding data source knowledge bases by statistical cycle | Data with different update cycles is stored in independent knowledge bases, matching differences in data update frequencies |
token_count_display | Enable separate counting for input and output | Adapts to user needs for verifying call costs, aligning with conventional configuration logic for conversational interactions |
The parameter values provided on this page are all common recommendations used as 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 Mistakes
- After configuring multiple knowledge bases, only the default knowledge base can be called, and retrieval results do not match the input filter conditions. Cause: The trigger logic for
knowledge_base_switchis not correctly configured, and the corresponding relationship between input parameters and knowledge bases is not bound. - Retrieval results only return content matched by a single vector, and cannot cover associated data for multiple SKUs. Cause: Multi-vector model recall rules are not configured, only recall parameters for a single vector model are bound, the scenario where one set of data corresponds to multiple vector sets is not handled, and multi-vector configuration logic for v4.8.7 and above is not adapted.
- The workflow HTTP module returns empty fields after receiving variables, and interface calls fail. Cause: Correct field mapping rules are not configured in
workflow_http_input_schema, and format requirements for required parameters are not declared.
How to Confirm Proper Configuration
- Enter the form configuration interface to verify whether the multi-level linked filtering function works normally, and whether the corresponding model list is automatically loaded after selecting a brand.
- Submit a simulated filter request to check whether the returned results match the selected statistical cycle, region, and brand range.
- Enable workflow debug mode to view the input and output logs of the HTTP module, and confirm whether variables are correctly mapped.
- Initiate a test conversation request to confirm whether the interface displays input and output token counts separately, and check whether the counting logic 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.