What the data for this category looks like
Data related to the profit yield of the white goods category comes from public in-store sales data from third-party home appliance retail monitoring agencies, compliant disclosed shipment cost data from brand parties, and public raw material quotes from commodity spot markets. This data is used for daily report broadcasts in financial wealth management scenarios. Update cadence: Real-time sales data from online e-commerce channels is updated daily. Weekly aggregated data is available for offline chain stores. Raw material costs are updated daily alongside commodity markets. Document structure: Each data entry includes fields such as brand name, SKU model, statistical cycle, terminal selling price, ex-factory cost, and per-unit gross profit. Corresponding units are yuan, unit, day/week, yuan/unit, yuan/unit, and yuan/unit respectively.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
Data sources are scattered, and update cadences differ. In multi-turn dialogue, users must be explicitly asked to specify a statistical cycle range. This prevents the model from mixing daily updated online data with weekly updated offline data, ensuring consistent data caliber for daily report broadcasts in financial wealth management scenarios. Fields include multiple numerical values related to cost and selling price. Prompts must explicitly limit usage to only retrieved public data fields, and must not fabricate unmentioned SKUs or numerical values. This ensures accuracy of wealth management data. Single data entries have high field density. Multi-turn dialogue context must retain sufficient historical information to avoid repeatedly requesting basic information such as brand and model from users, improving query efficiency. All data units are unified as yuan. Prompts must explicitly prohibit generating percentage-based profit yield statements, and only output corresponding values according to given fields. This complies with numerical display specifications for financial scenarios.
Configuration Settings
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The average length of single documents related to white goods data is several thousand characters. Sufficient context must be retained to accommodate multi-turn dialogue history and multiple retrieved SKU datasets. |
Recall count | Top 6–8 entries | The number of SKU categories covered in a single round of dialogue usually does not exceed 8. Excessive entries will exceed the model's processing limits. |
Similarity threshold | 0.72–0.85 | Low-match non-target home appliance SKU data must be filtered to avoid mixing in profit yield information from other categories. |
Chunk size | 1000–1500 characters | Matches the field density of single SKU data for white goods, ensuring each chunk contains complete brand, model, and profit-related fields. |
chatHistoryMaxCount | 10–15 turns | Limits the number of multi-turn dialogue history records to avoid redundant information interfering with processing of current queries. |
systemPromptTemplate | As determined through actual testing | Must be customized for the fields and update cadence of white goods, explicitly requiring only retrieved public data to be used, and no unmentioned information to be fabricated. |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: A prompt indicating no knowledge base selected appears during formal dialogue, but the workflow runs normally in the debug preview phase. Cause: In FastGPT V4.9.1, the formal deployment environment is not associated with the corresponding white goods profit yield knowledge base file, or the knowledge base association configuration is not synchronized to the deployment node.
- Phenomenon: Returned profit yield data in multi-turn dialogue includes information from non-target brands or SKUs. Cause: The
Similarity thresholdconfiguration is not set, or the threshold is set too low, resulting in non-target category home appliance data being mixed in during vector recall. - Phenomenon: The model confuses profit yield data from different statistical cycles during multi-turn dialogue. Cause: The prompt does not explicitly require marking the statistical cycle of the data, and does not limit the time range parameters of the dialogue context.
How to Verify Proper Configuration
- A query including a specific brand and SKU model is submitted, and returned data is checked to confirm it only includes public monitoring fields for the target category.
- Multiple consecutive related queries are submitted, and it is verified that the model retains brand and cycle information from the previous round of dialogue without repeated requests for basic information.
- Relevant RAG recall configurations are adjusted, and it is confirmed that the number and matching degree of returned results meet expectations.
- Knowledge base association settings of the deployment environment are checked to ensure that the formal environment and debug environment use the same knowledge base file.
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.